data

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.index filter=lfs diff=lfs merge=lfs -text

data/documents/content/0e22c68a-134e-4474-af0d-1c119b576bec.txt

@@ -0,0 +1,21 @@
+# MCP Chatbot
+
+1. Give me a list of tools you provide (in `connect_to_server_and_run`)
+
+## ListToolsRequest
+
+### MCP Client
+
+3. Tool definitions are exposed to the LLM (in `process_query`)
+
+### ListToolsResult
+
+2. Here are the tools I can run
+
+![img-0.jpeg](img-0.jpeg)
+
+![img-1.jpeg](img-1.jpeg)
+
+![img-2.jpeg](img-2.jpeg)
+
+![img-3.jpeg](img-3.jpeg)

data/documents/content/b229c249-b844-4579-beb1-2d4185eeb732.txt

@@ -0,0 +1,977 @@
+--- Page 1 ---
+Model Context Protocol (MCP): Landscape, Security Threats,
+and Future Research Directions
+XINYI HOU, Huazhong University of Science and Technology, China
+YANJIE ZHAO, Huazhong University of Science and Technology, China
+SHENAO WANG, Huazhong University of Science and Technology, China
+HAOYU WANG∗,Huazhong University of Science and Technology, China
+The Model Context Protocol (MCP) is a standardized interface designed to enable seamless interaction between
+AI models and external tools and resources, breaking down data silos and facilitating interoperability across
+diverse systems. This paper provides a comprehensive overview of MCP, focusing on its core components,
+workflow, and the lifecycle of MCP servers, which consists of three key phases: creation, operation, and update.
+We analyze the security and privacy risks associated with each phase and propose strategies to mitigate
+potential threats. The paper also examines the current MCP landscape, including its adoption by industry
+leaders and various use cases, as well as the tools and platforms supporting its integration. We explore future
+directions for MCP, highlighting the challenges and opportunities that will influence its adoption and evolution
+within the broader AI ecosystem. Finally, we offer recommendations for MCP stakeholders to ensure its secure
+and sustainable development as the AI landscape continues to evolve.
+CCS Concepts: •General and reference →Surveys and overviews ;•Security and privacy →Software
+and application security ;•Computing methodologies →Artificial intelligence .
+Additional Key Words and Phrases: Model Context Protocol, MCP, Vision paper, Security
+ACM Reference Format:
+Xinyi Hou, Yanjie Zhao, Shenao Wang, and Haoyu Wang. 2025. Model Context Protocol (MCP): Landscape,
+Security Threats, and Future Research Directions. 1, 1 (April 2025), 20 pages. https://doi.org/10.1145/nnnnnnn.
+nnnnnnn
+1 INTRODUCTION
+In recent years, the vision of autonomous AI agents capable of interacting with a wide range of
+tools and data sources has gained significant momentum. This progress accelerated in 2023 with the
+introduction of function calling by OpenAI, which allowed language models to invoke external
+APIs in a structured way [ 38]. This advancement expanded the capabilities of LLMs, enabling
+them to retrieve real-time data, perform computations, and interact with external systems. As
+function calling gained adoption, an ecosystem formed around it. OpenAI introduced the ChatGPT
+plugin [37], allowing developers to build callable tools for ChatGPT. LLM app stores such as Coze [ 4]
+and Yuanqi [ 50] have launched their plugin stores , supporting tools specifically designed for
+∗Haoyu Wang is the corresponding author (haoyuwang@hust.edu.cn).
+Authors’ addresses: Xinyi Hou, xinyihou@hust.edu.cn, Huazhong University of Science and Technology, Wuhan, China;
+Yanjie Zhao, yanjie_zhao@hust.edu.cn, Huazhong University of Science and Technology, Wuhan, China; Shenao Wang,
+shenaowang@hust.edu.cn, Huazhong University of Science and Technology, Wuhan, China; Haoyu Wang, haoyuwang@
+hust.edu.cn, Huazhong University of Science and Technology, Wuhan, China.
+Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee
+provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the
+full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored.
+Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires
+prior specific permission and/or a fee. Request permissions from permissions@acm.org.
+©2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
+ACM XXXX-XXXX/2025/4-ART
+https://doi.org/10.1145/nnnnnnn.nnnnnnn
+, Vol. 1, No. 1, Article . Publication date: April 2025.arXiv:2503.23278v2  [cs.CR]  6 Apr 2025
+
+--- Page 2 ---
+2 X Hou, Y Zhao, S Wang, and H Wang
+their platforms. Frameworks like LangChain [ 26] and LlamaIndex [ 29] provided standardized tool
+interfaces , making it easier to integrate LLMs with external services. Other AI providers, including
+Anthropic, Google, and Meta, introduced similar mechanisms, further driving adoption. Despite
+these advancements, integrating tools remains fragmented . Developers must manually define
+interfaces, manage authentication, and handle execution logic for each service. Function calling
+mechanisms vary across platforms, requiring redundant implementations. Additionally, current
+approaches rely on predefined workflows, limiting AI agents’ flexibility in dynamically
+discovering and orchestrating tools .
+In late 2024, Anthropic introduced the Model Context Protocol (MCP)[ 3], a general-purpose pro-
+tocol standardizing AI-tool interactions. Inspired by the Language Server Protocol (LSP) [ 22], MCP
+provides a flexible framework for AI applications to communicate with external tools dynamically.
+Instead of relying on predefined tool mappings, MCP allows AI agents to autonomously discover,
+select, and orchestrate tools based on task context. It also supports human-in-the-loop mechanisms,
+enabling users to inject data or approve actions as needed. By unifying interfaces, MCP simplifies
+the development of AI applications and improves their flexibility in handling complex workflows.
+Since its release, MCP has rapidly grown from a niche protocol to a key foundation for AI-native
+application development. A thriving ecosystem has emerged, with thousands of community-driven
+MCP servers enabling model access to systems like GitHub [ 41], Slack [ 42], and even 3D design tools
+like Blender [ 1]. Tools like Cursor [ 12] and Claude Desktop [ 2] demonstrate how MCP clients can
+extend their capabilities by installing new servers, turning developer tools, productivity platforms,
+and creative environments alike into multi-modal AI agents.
+Despite the rapid adoption of MCP, its ecosystem is still in the early stages, with key areas such
+as security, tool discoverability, and remote deployment lacking comprehensive solutions. These
+issues present untapped opportunities for further research and development. Although MCP is
+widely recognized for its potential in the industry, it has not yet been extensively analyzed in
+academic research. This gap in research motivates this paper, which provides the first analysis of
+the MCP ecosystem, examining its architecture and workflow, defining the lifecycle of MCP servers,
+and identifying potential security risks at each stage, such as installer spoofing and tool name
+conflict. Through this study, we present a thorough exploration of MCP’s current landscape and
+offer a forward-looking vision that highlights key implications, outlines future research directions,
+and addresses the challenges that must be overcome to ensure its sustainable growth.
+Our contributions are as follows:
+(1)We provide the first analysis of the MCP ecosystem, detailing its architecture, components,
+and workflow.
+(2)We identify the key components of MCP servers and define their lifecycle, encompassing the
+stages of creation, operation, and update. We also highlight potential security risks associated
+with each phase, offering insights into safeguarding AI-to-tool interactions.
+(3)We examine the current MCP ecosystem landscape, analyzing the adoption, diversity, and
+use cases across various industries and platforms.
+(4)We discuss the implications of MCP’s rapid adoption, identifying key challenges for stake-
+holders, and outline future research directions on security, scalability, and governance to
+ensure its sustainable growth.
+The remainder of this paper is structured as follows: § 2 compares tool invocation with and
+without MCP, highlighting the motivation for this study. § 3 outlines the architecture of MCP,
+detailing the roles of the MCP host, client, and server, as well as the lifecycle of the MCP server.
+§ 4 examines the current MCP landscape, focusing on key industry players and adoption trends.
+§ 5 analyzes security and privacy risks across the MCP server lifecycle and proposes mitigation
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 3 ---
+Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 3
+strategies. § 6 explores implications, future challenges, and recommendations to enhance MCP’s
+scalability and security in dynamic AI environments. § 7 reviews prior work on tool integration
+and security in LLM applications. Finally, § 8 concludes the whole paper.
+2 BACKGROUND AND MOTIVATION
+2.1 AI Tooling
+Before the introduction of MCP, AI applications relied on various methods, such as manual API
+wiring, plugin-based interfaces, and agent frameworks, to interact with external tools. As shown in
+Figure 1, these approaches required integrating each external service with a specific API, leading to
+increased complexity and limited scalability. MCP addresses these challenges by providing a
+standardized protocol that enables seamless and flexible interaction with multiple tools.
+AIApplication
+External Tools and ResourcesSpecific APISpecific APISpecific APIWeb ServicesMCPServerAIApplication(MCPClient)Model Context Protocolvs.WithoutMCPWithMCP
+Database
+LocalFiles
+Specific APISpecific APISpecific APIExternal Tools and ResourcesWeb Services
+Database
+LocalFiles
+Fig. 1. Tool invocation with and without MCP.
+2.1.1 Manual API Wiring. In traditional implementations, developers had to establish manual API
+connections for each tool or service that an AI application interacted with. This process required
+custom authentication, data transformation, and error handling for every integration . As
+the number of APIs increased, the maintenance burden became significant, often leading to tightly
+coupled and fragile systems that were difficult to scale or modify. MCP eliminates this complexity
+by offering a unified interface, allowing AI models to connect with multiple tools dynamically
+without the need for custom API wiring.
+2.1.2 Standardized Plugin Interfaces. To reduce the complexity of manual wiring, plugin-based
+interfaces such as OpenAI ChatGPT Plugins, introduced in November 2023 [ 37], allowed AI models
+to connect with external tools through standardized API schemas like OpenAPI. For example, in
+the OpenAI Plugin ecosystem, plugins like Zapier allowed models to perform predefined actions,
+such as sending emails or updating CRM records. However, these interactions were often one-
+directional and could not maintain state or coordinate multiple steps in a task . New LLM
+app stores [ 62] such as ByteDance Coze [ 4] and Tencent Yuanqi [ 50] have also emerged, offering a
+plugin store for web services. While these platforms expanded available tool options, they created
+isolated ecosystems where plugins are platform-specific , limiting cross-platform compatibility
+and requiring duplicate maintenance efforts. MCP stands out by being open-source and platform-
+agnostic, enabling AI applications to engage in rich two-way interactions with external tools,
+facilitating complex workflows.
+2.1.3 AI Agent Tool Integration. The emergence of AI agent frameworks like LangChain [ 26] and
+similar tool orchestration frameworks provided a structured way for models to invoke external tools
+through predefined interfaces, improving automation and adaptability [ 55]. However, integrating
+and maintaining these tools remained largely manual, requiring custom implementations and
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 4 ---
+4 X Hou, Y Zhao, S Wang, and H Wang
+increasing complexity as the number of tools grew. MCP simplifies this process by offering a
+standardized protocol that enables AI agents to seamlessly invoke, interact with, and
+chain multiple tools through a unified interface . This reduces manual configuration and
+enhances task flexibility, allowing agents to perform complex operations without extensive custom
+integration.
+2.1.4 Retrieval-Augmented Generation (RAG) and Vector Database. Contextual information retrieval
+methods, such as RAG, leverage vector-based search to retrieve relevant knowledge from databases
+or knowledge bases, enabling models to supplement responses with up-to-date information [ 11,16].
+While this approach addressed the problem of knowledge cutoff and improved model accuracy, it
+was limited to passive retrieval of information . It did not inherently allow models to perform
+active operations, such as modifying data or triggering workflows. For example, a RAG-based
+system could retrieve relevant sections from a product documentation database to assist a customer
+support AI. However, if the AI needed to update customer records or escalate an issue to human
+support, it could not take action beyond providing textual responses. MCP extends beyond passive
+information retrieval by enabling AI models to interact with external data sources and tools actively,
+facilitating both retrieval and action in a unified workflow.
+2.2 Motivation
+MCP has rapidly gained traction in the AI community due to its ability to standardize how AI
+models interact with external tools, fetch data, and execute operations. By addressing the limitations
+of manual API wiring, plugin interfaces, and agent frameworks, MCP has the potential to redefine
+AI-to-tool interactions and enable more autonomous and intelligent agent workflows. Despite
+its growing adoption and promising potential, MCP is still in its early stages, with an evolving
+ecosystem that remains incomplete. Many key aspects, such as security and tool discoverability,
+are yet to be fully addressed, leaving ample room for future research and improvement. Moreover,
+while MCP has gained rapid adoption in the industry, it is still largely unexplored in academia.
+Motivated by this gap, this paper is the first to analyze the current MCP landscape, examine
+its emerging ecosystem, and identify potential security risks . Additionally, we outline a
+vision for MCP’s future development and highlight the key challenges that must be addressed to
+support its long-term success.
+3 MCP ARCHITECTURE
+3.1 Core Components
+The MCP architecture is composed of three core components: MCP host ,MCP client , and MCP
+server . These components collaborate to facilitate seamless communication between AI applications,
+external tools, and data sources, ensuring that operations are secure and properly managed. As
+shown in Figure 2, in a typical workflow, the user sends a prompt to the MCP client, which analyzes
+the intent ,selects the appropriate tools via the MCP server, and invokes external APIs to
+retrieve and process the required information before notifying the user of the results.
+3.1.1 MCP Host. The MCP host is an AI application that provides the environment for executing
+AI-based tasks while running the MCP client. It integrates interactive tools and data to enable
+smooth communication with external services. Examples include Claude Desktop for AI-assisted
+content creation, Cursor, an AI-powered IDE for code completion and software development, and
+AI agents that function as autonomous systems for executing complex tasks. The MCP host hosts
+the MCP client and ensures communication with external MCP servers.
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 5 ---
+Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 5
+MCP ClientsPrompt:“Can you please fetch the latest stock price of AAPL and notify me via email?”MCP Servers
+CapabilitiesToolsResourcesPrompts
+②InitialResponse③Notification①InitialRequestTransferLayerDataSourceWeb Services
+Database
+LocalFiles
+Tool Selection
+API Invocation
+IntentAnalysisNotificationSampling
+Orchestration
+UserMCP Workflow
+(ChatApps,IDEs,···,AI Agents)MCPHosts
+1:1Client
+Server
+Fig. 2. The workflow of MCP.
+3.1.2 MCP Client. The MCP client acts as an intermediary within the host environment, managing
+communication between the MCP host and one or more MCP servers. It initiates requests to MCP
+servers, queries available functions, and retrieves responses that describe the server’s capabilities.
+This ensures seamless interaction between the host and external tools. In addition to managing
+requests and responses, the MCP client processes notifications from MCP servers, providing
+real-time updates about task progress and system status. It also performs sampling to gather data
+on tool usage and performance, enabling optimization and informed decision-making. The MCP
+client communicates through the transport layer with MCP servers, facilitating secure, reliable
+data exchange and smooth interaction between the host and external resources.
+3.1.3 MCP Server. The MCP server enables the MCP host and client to access external systems
+and execute operations, offering three core capabilities: tools, resources, and prompts .
+•Tools: Enabling external operations . Tools allow the MCP server to invoke external services
+and APIs to execute operations on behalf of AI models. When the client requests an operation, the
+MCP server identifies the appropriate tool, interacts with the service, and returns the result. For
+instance, if an AI model requires real-time weather data or sentiment analysis, the MCP server
+connects to the relevant API, retrieves the data, and delivers it to the host. Unlike traditional
+function calling, which requires multiple steps and separates invocation from execution, Tools of
+MCP servers streamline this process by allowing the model to autonomously select and invoke
+the appropriate tool based on context. Once configured, these tools follow a standardized supply-
+and-consume model, making them modular, reusable, and easily accessible to other applications,
+enhancing system efficiency and flexibility.
+•Resources: Exposing data to AI models . Resources provide access to structured and unstruc-
+tured datasets that the MCP server can expose to AI models. These datasets may come from
+local storage, databases, or cloud platforms. When an AI model requests specific data, the MCP
+server retrieves and processes the relevant information, enabling the model to make data-driven
+decisions. For example, a recommendation system may access customer interaction logs, or a
+document summarization task may query a text repository.
+•Prompts: Reusable templates for workflow optimization . Prompts are predefined templates
+and workflows that the MCP server generates and maintains to optimize AI responses and
+streamline repetitive tasks. They ensure consistency in responses and improve task execution
+efficiency. For instance, a customer support chatbot may use prompt templates to provide uniform
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 6 ---
+6 X Hou, Y Zhao, S Wang, and H Wang
+and accurate responses, while an annotation task may rely on predefined prompts to maintain
+consistency in data labeling.
+3.2 Transport Layer and Communication
+The transport layer ensures secure, bidirectional communication, allowing for real-time interaction
+and efficient data exchange between the host environment and external systems. The transport
+layer manages the transmission of initial requests from the client, the delivery of server responses
+detailing available capabilities, and the exchange of notifications that keep the client informed of
+ongoing updates. Communication between the MCP client and the MCP server follows a structured
+process, beginning with an initial request from the client to query the server’s functionalities.
+Upon receiving the request, the server responds with an initial response listing the available
+tools, resources, and prompts the client can leverage. Once the connection is established, the
+system maintains a continuous exchange of notifications to ensure that changes in server status
+or updates are communicated back to the client in real time. This structured communication
+ensures high-performance interactions and keeps AI models synchronized with external resources,
+enhancing the effectiveness of AI applications.
+3.3 MCP Server Lifecycle
+The MCP server lifecycle as shown in Figure 3 consists of three key phases: creation ,operation ,
+andupdate . Each phase defines critical activities that ensure the secure and efficient functioning
+of the MCP server, enabling seamless interaction between AI models and external tools, resources,
+and prompts.
+CreationMCPServerComponentsConfigurationSource CodeConfig FilesManifest
+MetadataNameDescriptionVersion
+Tool ListTool NameDescriptionPermissions
+Resources ListData SourcesEndpointsPermissions
+PromptsTemplatesWorkflowsMetadata
+Server Registration → Name CollisionInstaller Deployment → Installer SpoofingCode Integrity Verification → BackdoorOperationTool Execution → Tool Name ConflictSlash Command → Command OverlapSandbox Mechanism → Sandbox EscapeUpdateAuthorization → Privilege PersistenceVersion Control → Vulnerable VersionsOld Version → Configuration DriftMCPServer Lifecycle
+Fig. 3. MCP servers components and lifecycle.
+3.3.1 MCP Server Components. The MCP server is responsible for managing external tools, data
+sources, and workflows, providing AI models with the necessary resources to perform tasks
+efficiently and securely. It comprises several key components that ensure smooth and effective
+operations. Metadata includes essential information about the server, such as its name, version, and
+description, allowing clients to identify and interact with the appropriate server. Configuration
+involves the source code, configuration files, and manifest, which define the server’s operational
+parameters, environment settings, and security policies. Tool list stores a catalog of available tools,
+detailing their functionalities, input-output formats, and access permissions, ensuring proper tool
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 7 ---
+Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 7
+management and security. Resources list governs access to external data sources, including web
+APIs, databases, and local files, specifying allowed endpoints and their associated permissions.
+Finally, Prompts and Templates include pre-configured task templates and workflows that
+enhance the efficiency of AI models in executing complex operations. Together, these components
+enable MCP servers to provide seamless tool integration, data retrieval, and task orchestration for
+AI-powered applications.
+3.3.2 Creation Phase. The creation phase is the initial stage of the MCP server lifecycle, where the
+server is registered, configured, and prepared for operation. This phase involves three key steps.
+Server registration assigns a unique name and identity to the MCP server, allowing clients to
+discover and connect to the appropriate server instance. Installer deployment involves installing
+the MCP server and its associated components, ensuring that the correct configuration files, source
+code, and manifests are in place. Code integrity verification validates the integrity of the server’s
+codebase to prevent unauthorized modifications or tampering before the server becomes operational.
+Successful completion of the creation phase ensures that the MCP server is ready to handle requests
+and interact securely with external tools and data sources.
+3.3.3 Operation Phase. The operation phase is where the MCP server actively processes requests,
+executes tool invocations, and facilitates seamless interaction between AI applications and external
+resources. Tool execution allows the MCP server to invoke the appropriate tools based on the AI
+application’s requests, ensuring that the selected tools perform their intended operations. Slash
+command handling enables the server to interpret and execute multiple commands, including
+those issued through user interfaces or AI agents, while managing potential command overlaps to
+prevent conflicts. Sandbox mechanism enforcement ensures that the execution environment is
+isolated and secure, preventing unauthorized access and mitigating potential risks. Throughout the
+operation phase, the MCP server maintains a stable and controlled environment, enabling reliable
+and secure task execution.
+3.3.4 Update Phase. The update phase ensures that the MCP server remains secure, up-to-date, and
+capable of adapting to evolving requirements. This phase includes three key tasks. Authorization
+management verifies that post-update access permissions remain valid, preventing unauthorized
+use of server resources after updates. Version control maintains consistency between different
+server versions, ensuring that new updates do not introduce vulnerabilities or conflicts. Old version
+management deactivates or removes outdated versions to prevent attackers from exploiting known
+vulnerabilities in previous versions.
+Understanding the MCP server lifecycle is essential for identifying potential vulnerabilities
+and designing effective security measures. Each phase introduces distinct challenges that must
+be carefully addressed to maintain the security, efficiency, and adaptability of the MCP server in
+dynamic AI environments.
+4 CURRENT LANDSCAPE
+4.1 Ecosystem Overview
+4.1.1 Key Adopters. Table 1 demonstrates how MCP has gained significant traction across diverse
+sectors, signaling its growing importance in enabling seamless AI-to-tool interactions. Notably,
+leading AI companies such as Anthropic [ 2] and OpenAI [ 39] have integrated MCP to enhance
+agent capabilities and improve multi-step task execution. This adoption by industry pioneers
+has set a precedent, encouraging other major players to follow suit. Chinese tech giants like
+Baidu [ 31] have also incorporated MCP into their ecosystems, highlighting the protocol’s potential
+to standardize AI workflows across global markets. Developer tools and IDEs, including Replit [ 43],
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 8 ---
+8 X Hou, Y Zhao, S Wang, and H Wang
+Table 1. Overview of MCP ecosystem adoption.
+Category Company/Product Key Features or Use Cases
+Anthropic (Claude) [2] Full MCP support in the desktop version, enabling interaction with external tools.
+AI Models and Frameworks OpenAI [39] MCP support in Agent SDK and API for seamless integration.
+Baidu Maps [31] API integration using MCP to access geolocation services.
+Blender MCP [33] Enables Blender and Unity 3D model generation via natural language commands.
+Replit [43] AI-assisted development environment with MCP tool integration.
+Microsoft Copilot Studio [49] Extends Copilot Studio with MCP-based tool integration.
+Developer Tools Sourcegraph Cody [10] Implements MCP through OpenCTX for resource integration.
+Codeium [9] Adds MCP support for coding assistants to facilitate cross-system tasks.
+Cursor [12] MCP tool integration in Cursor Composer for seamless code execution.
+Cline [7] VS Code coding agent that manages MCP tools and servers.
+Zed [60] Provides slash commands and tool integration based on MCP.
+JetBrains [24] Integrates MCP for IDE-based AI tooling.
+IDEs/Editors Windsurf Editor [14] AI-assisted IDE with MCP tool interaction.
+TheiaAI/TheiaIDE [52] Enables MCP server interaction for AI-powered tools.
+Emacs MCP [32] Enhances AI functionality in Emacs by supporting MCP tool invocation.
+OpenSumi [40] Supports MCP tools in IDEs and enables seamless AI tool integration.
+Cloudflare [8] Provides remote MCP server hosting and OAuth integration.
+Cloud Platforms and Services Block (Square) [47] Uses MCP to enhance data processing efficiency for financial platforms.
+Stripe [48] Exposes payment APIs via MCP for seamless AI integration.
+Apify MCP Tester [51] Connects to any MCP server using SSE for API testing.
+Web Automation and Data LibreChat [28] Extends the current tool ecosystem through MCP integration.
+Goose [21] Allows building AI agents with integrated MCP server functionality.
+Microsoft Copilot Studio [ 49], JetBrains [ 24], and TheiaIDE [ 52], leverage MCP to facilitate agentic
+workflows and streamline cross-platform operations. This trend indicates a shift toward embedding
+MCP in developer environments to enhance productivity and reduce manual integration efforts.
+Furthermore, cloud platforms like Cloudflare [ 8] and financial service providers such as Block
+(Square) [ 47] and Stripe [ 48] are exploring MCP to improve security, scalability, and governance
+in multi-tenant environments. The widespread adoption of MCP by these industry leaders not
+only highlights its growing relevance but also points to its potential as a foundational layer in
+AI-powered ecosystems. As more companies integrate MCP into their operations, the protocol is
+set to play a central role in shaping the future of AI tool integration. Looking ahead, MCP is poised
+to become a key enabler of AI-driven workflows, driving more secure, scalable, and efficient AI
+ecosystems across industries.
+4.1.2 Community-Driven MCP Servers. Anthropic has not yet released an official MCP marketplace,
+but the vibrant MCP community has stepped in to fill this gap by creating numerous independent
+server collections and platforms. As shown in Table 2, platforms such as MCP.so [ 35], Glama [ 20],
+and PulseMCP [ 15] host thousands of servers, allowing users to discover and integrate a wide
+range of tools and services. These community-driven platforms have significantly accelerated the
+adoption of MCP by providing accessible repositories where developers can publish, manage, and
+share their MCP servers. Desktop-based solutions like Dockmaster [ 34] and Toolbase [ 19] further
+enhance local MCP deployment capabilities, empowering developers to manage and experiment
+with servers in isolated environments. The rise of community-driven MCP server ecosystems
+reflects the growing enthusiasm for MCP and highlights the need for a formalized marketplace.
+4.1.3 SDKs and Tools. With the continuous growth of community-driven tools and official SDKs,
+the MCP ecosystem is becoming increasingly accessible, allowing developers to integrate MCP into
+various applications and workflows efficiently. Official SDKs are available in multiple languages,
+including TypeScript ,Python ,Java ,Kotlin , and C#, providing developers with versatile options
+to implement MCP in different environments. In addition to official SDKs, the community has
+contributed numerous frameworks and utilities that simplify MCP server development. Tools such
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 9 ---
+Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 9
+Table 2. Overview of MCP server collections and deployment modes (As of March 27, 2025).
+Collection Author Mode # Servers URL
+MCP.so mcp.so Website 4774 mcp.so
+Glama glama.ai Website 3356 glama.ai
+PulseMCP Antanavicius et al. Website 3164 pulsemcp.com
+Smithery Henry Mao Website 2942 smithery.ai
+Dockmaster mcp-dockmaster Desktop App 517 mcp-dockmaster.com
+Official Collection Anthropic GitHub Repo 320 modelcontextprotocol/servers
+AiMCP Hekmon Website 313 aimcp.info
+MCP.run mcp.run Website 114 mcp.run
+Awesome MCP Servers Stephen Akinyemi GitHub Repo 88 appcypher/mcp-servers
+mcp-get registry Michael Latman Website 59 mcp-get.com
+Awesome MCP Servers wong2 Website 34 mcpservers.org
+OpenTools opentoolsteam Website 25 opentools.com
+Toolbase gching Desktop App 24 gettoolbase.ai
+make inference mkinf Website 20 mkinf.io
+Awesome Crypto MCP Servers Luke Fan GitHub Repo 13 badkk/crypto-mcp-servers
+asEasyMCP andFastMCP offer lightweight TypeScript-based solutions for quickly building MCP
+servers, while FastAPI to MCP Auto Generator enables the seamless exposure of FastAPI endpoints
+as MCP tools. For more complex scenarios, Foxy Contexts provides a Golang-based library to
+build MCP servers, and Higress MCP Server Hosting extends the API Gateway (based on Envoy)
+to host MCP servers with wasm plugins. Server generation and management platforms such as
+Mintlify ,Speakeasy , and Stainless further enhance the ecosystem by automating MCP server
+generation , providing curated MCP server lists, and enabling faster deployment with minimal
+manual intervention. These platforms empower organizations to rapidly create and manage secure
+and well-documented MCP servers.
+4.2 Use Cases
+MCP has become a vital tool for AI applications to effectively communicate with external tools,
+APIs, and systems. By standardizing interactions, MCP simplifies complex workflows, boosting the
+efficiency of AI-driven applications. Below, we explore three key platforms (i.e., OpenAI, Cursor,
+and Cloudflare) that have successfully integrated MCP, highlighting their distinct use cases.
+4.2.1 OpenAI: MCP Integration in AI Agents and SDKs. OpenAI has adopted MCP to standardize
+AI-to-tool communication, recognizing its potential to enhance integration with external tools.
+Recently, OpenAI introduced MCP support in its Agent SDK, enabling developers to create AI
+agents that seamlessly interact with external tools. In a typical workflow, developers use the Agent
+SDK to define tasks that require external tool invocation. When an AI agent encounters a task
+like retrieving data from an API or querying a database, the SDK routes the request through an
+MCP server. The request is transmitted via the MCP protocol, ensuring proper formatting and
+real-time response delivery to the agent. OpenAI’s plan to integrate MCP into the Responses API
+will streamline AI-to-tool communication, allowing AI models like ChatGPT to interact with tools
+dynamically without extra configuration. Additionally, OpenAI aims to extend MCP support to
+ChatGPT desktop applications, enabling AI assistants to handle various user tasks by connecting
+to remote MCP servers, further bridging the gap between AI models and external systems.
+4.2.2 Cursor: Enhancing Software Development with MCP-Powered Code Assistants. Cursor uses
+MCP to enhance software development by enabling AI-powered code assistants that automate
+complex tasks. With MCP, Cursor allows AI agents to interact with external APIs, access code
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 10 ---
+10 X Hou, Y Zhao, S Wang, and H Wang
+repositories, and automate workflows directly within the integrated development environment.
+When a developer issues a command within the IDE, the AI agent evaluates whether external tools
+are needed. If so, the agent sends a request to an MCP server, which identifies the appropriate
+tool and processes the task, such as running API tests, modifying files, or analyzing code. The
+results are then returned to the agent for further action. This integration helps automate repetitive
+tasks, minimizing errors and enhancing overall development efficiency. By simplifying complex
+processes, Cursor boosts both productivity and accuracy, allowing developers to execute multi-step
+operations effortlessly.
+4.2.3 Cloudflare: Remote MCP Server Hosting and Scalability. Cloudflare has played a pivotal role
+in transforming MCP from a local deployment model to a cloud-hosted architecture by introducing
+remote MCP server hosting. This approach eliminates the complexities associated with configuring
+MCP servers locally, allowing clients to connect to secure, cloud-hosted MCP servers seamlessly.
+The workflow begins with Cloudflare hosting MCP servers in secure cloud environments that are
+accessible via authenticated API calls. AI agents initiate requests to the Cloudflare MCP server
+using OAuth-based authentication, ensuring that only authorized entities can access the server.
+Once authenticated, the agent dynamically invokes external tools and APIs through the MCP server,
+executing tasks such as data retrieval, document processing, or API integration. This approach
+not only reduces the risk of misconfiguration but also ensures seamless execution of AI-powered
+workflows across distributed environments. Furthermore, Cloudflare’s multi-tenant architecture
+allows multiple users to securely access and manage their own MCP instances, ensuring isolation
+and preventing data leakage. Cloudflare’s solution thus extends MCP’s capabilities by enabling
+enterprise-grade scalability and secure multi-device interoperability.
+The adoption of MCP by platforms like OpenAI, Cursor, and Cloudflare highlights its flexibility
+and growing role in AI-driven workflows, enhancing efficiency, adaptability, and scalability across
+development tools, enterprise applications, and cloud services.
+5 SECURITY AND PRIVACY ANALYSIS
+MCP servers, as open and extensible platforms, introduce various security risks throughout their
+lifecycle. In this section, we analyze security threats across different phases: creation ,operation ,
+andupdate . Each phase of the MCP server lifecycle presents unique challenges that, if not properly
+mitigated, can compromise system integrity, data security, and user privacy.
+5.1 Security Risks in the Creation Phase
+The creation phase of an MCP server involves registering the server, deploying the installer, and
+verifying code integrity. This phase introduces three key risks: name collision, installer spoofing,
+and code injection/backdoor.
+5.1.1 Name Collision. Server name collision occurs when a malicious entity registers an MCP
+server with an identical or deceptively similar name to a legitimate server, deceiving users during the
+installation phase. Since MCP clients primarily rely on the server’s name and description when
+selecting servers , they are vulnerable to such impersonation attacks. Once a compromised server is
+installed, it can mislead AI agents and clients into invoking the malicious server, potentially exposing
+sensitive data, executing unauthorized commands, or disrupting workflows. For example, an attacker
+could register a server named mcp-github that mimics the legitimate github-mcp server, allowing
+them to intercept and manipulate sensitive interactions between AI agents and trusted services.
+Although MCP currently operates primarily in local environments, future adoption in multi-
+tenant environments introduces additional risks of name collision. In these scenarios, where
+multiple organizations or users might register servers with similar names, the lack of centralized
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 11 ---
+Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 11
+naming control can increase the likelihood of confusion and impersonation attacks. Additionally,
+asMCP marketplaces grow to support public server listings, supply chain attacks may
+become a critical concern , where malicious servers can replace legitimate ones. To mitigate
+these risks, future research can focus on establishing strict namespace policies, implementing
+cryptographic server verification, and designing reputation-based trust systems to secure MCP
+server registrations.
+5.1.2 Installer Spoofing. Installer spoofing occurs when attackers distribute modified MCP server
+installers that introduce malicious code or backdoors during the installation process. Each MCP
+server requires a unique configuration that users must manually set up in their local environments
+before the client can invoke the server. This manual configuration process creates a barrier for
+less technical users, prompting the emergence of unofficial auto-installers that automate the
+setup process. As shown in Table 3, tools such as Smithery-CLI ,mcp-get , and mcp-installer
+streamline the installation process, allowing users to quickly configure MCP servers without dealing
+with intricate server settings.
+Table 3. Unofficial MCP auto installers (As of March 27, 2025).
+Tool Author # Stars # Servers URL
+Smithery CLI Henry Mao 170 2942 smithery.ai
+mcp.run Dylibso / 118 docs.mcp.run
+mcp-get Michael Latman 318 59 mcp-get.com
+Toolbase gching / 24 gettoolbase.ai
+mcp-installer Ani Betts 767 NL1mcp-installer
+1Enables MCP server installation through natural language interaction with the
+client.
+However, while these auto-installers enhance usability, they also introduce new attack surfaces
+by potentially distributing compromised packages. Since these unofficial installers are often sourced
+from unverified repositories or community-driven platforms, they may inadvertently expose users
+to security risks such as installing tampered servers or misconfigured environments. Attackers
+canexploit these auto-installers by embedding malware that grants unauthorized access,
+modifies system configurations, or creates persistent backdoors . Moreover, most users
+who opt for one-click installations rarely review the underlying code for potential security
+vulnerabilities, making it easier for attackers to distribute compromised versions undetected.
+Addressing these challenges requires developing a standardized, secure installation framework
+for MCP servers, enforcing package integrity checks, and establishing reputation-based trust
+mechanisms to assess the credibility of auto-installers in the MCP ecosystem.
+5.1.3 Code Injection/Backdoor. Code injection attacks occur when malicious code is surreptitiously
+embedded into the MCP server’s codebase during the creation phase, often bypassing traditional
+security checks. It targets the server’s source code or configuration files, embedding hidden back-
+doors that persist even after updates or security patches. These backdoors allow attackers to
+silently maintain control over the server, enabling actions such as unauthorized data exfiltration,
+privilege escalation, or command manipulation. Code injection is particularly insidious because
+it can be introduced by compromised dependencies, vulnerable build pipelines, or unauthorized
+modifications to the server’s source code. Since MCP servers often rely on community-maintained
+components and open-source libraries, ensuring the integrity of these dependencies is critical.
+To mitigate this risk, rigorous code integrity verification, strict dependency management,
+and regular security audits should be implemented to detect unauthorized modifications
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 12 ---
+12 X Hou, Y Zhao, S Wang, and H Wang
+and prevent the introduction of malicious code . Additionally, adopting reproducible builds
+and enforcing checksum validation during deployment can further safeguard MCP servers from
+injection-based threats.
+5.2 Security Risks in the Operation Phase
+The operation phase is when the MCP server actively executes tools, processes slash commands,
+and interacts with external APIs. This phase introduces three major risks: tool name conflicts, slash
+command overlap, and sandbox escape.
+5.2.1 Tool Name Conflicts. Tool name conflicts arise when multiple tools within the MCP ecosystem
+share identical or similar names, leading to ambiguity and confusion during tool selection and
+execution. This can result in AI applications inadvertently invoking the wrong tool, potentially
+executing malicious commands or leaking sensitive information. A common attack scenario involves
+a malicious actor registering a tool named send_email that mimics a legitimate email-sending tool.
+If the MCP client invokes the malicious version, sensitive information intended for trusted recipients
+may be redirected to an attacker-controlled endpoint, compromising data confidentiality. Beyond
+name similarity, our experiments revealed that malicious actors can further manipulate tool
+selection by embedding deceptive phrases in tool descriptions. Specifically, we observed that if a
+tool’s description explicitly contains directives like “this tool should be prioritized” or “prefer using
+this tool first”, the MCP client is more likely to select that tool, even when its functionality is inferior
+or potentially harmful. This introduces a severe risk of toolflow hijacking , where attackers can
+leverage misleading descriptions to influence tool selection and gain control over critical workflows.
+This underscores the need for researchers to develop advanced validation and anomaly detection
+techniques to identify and mitigate deceptive tool descriptions, ensuring accurate and secure AI
+tool selection.
+5.2.2 Slash Command Overlap. Slash command overlap occurs when multiple tools define identical
+or similar commands, leading to ambiguity during command execution. This overlap introduces
+the risk of executing unintended actions, especially when AI applications dynamically select and
+invoke tools based on contextual cues. Malicious actors can exploit this ambiguity by introducing
+conflicting commands that manipulate tool behavior, potentially compromising system integrity or
+exposing sensitive data. For instance, if one tool registers a /delete command to remove temporary
+files while another uses the same command to erase critical system logs, an AI application may
+mistakenly execute the incorrect command, potentially causing data loss or system instability.
+Similar issues have been observed in team chat systems such as Slack, where overlapping command
+registrations allowed unauthorized tools to hijack legitimate invocations, resulting in security
+breaches and operational disruptions [ 61]. Since slash commands are often surfaced as user-
+facing shortcuts in client interfaces, misinterpreted or conflicting commands can lead to
+dangerous outcomes , especially in multi-tool environments. To minimize this risk, MCP clients
+should establish context-aware command resolution, apply command disambiguation techniques,
+and prioritize execution based on verified tool metadata.
+5.2.3 Sandbox Escape. Sandboxing isolates the execution environment of MCP tools, restricting
+their access to critical system resources and protecting the host system from potentially harmful
+operations. However, sandbox escape vulnerabilities arise when attackers exploit flaws in the
+sandbox implementation, enabling them to break out of the restricted environment and gain
+unauthorized access to the host system. Once outside the sandbox, attackers can execute arbitrary
+code, manipulate sensitive data, or escalate privileges, compromising the security and stability of the
+MCP ecosystem. Common attack vectors include exploiting weaknesses in system calls, improperly
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 13 ---
+Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 13
+handled exceptions, and vulnerabilities in third-party libraries. For instance, a malicious MCP tool
+could exploit unpatched vulnerabilities in the underlying container runtime to bypass confinement
+and execute commands with elevated privileges. Similarly, side-channel attacks may allow attackers
+to extract sensitive data, undermining the intended isolation of the sandbox. Examining real-world
+sandbox escape scenarios in MCP environments can provide valuable insights for strengthening
+sandbox security and preventing future exploitation.
+5.3 Security Risks in the Update Phase
+The update phase involves managing server versions, modifying configurations, and adjusting
+access controls. This phase introduces three critical risks: post-update privilege persistence, re-
+deployment of vulnerable versions, and configuration drift.
+5.3.1 Post-Update Privilege Persistence. Post-update privilege persistence occurs when outdated
+or revoked privileges remain active after an MCP server update, allowing previously authorized
+users or malicious actors to retain elevated privileges. This vulnerability arises when privilege
+modifications, such as API key revocations or permission changes, are not properly synchro-
+nized or invalidated following server updates . If these outdated privileges persist, attackers
+may exploit them to maintain unauthorized access to sensitive resources or perform malicious
+operations. For example, in API-driven environments like GitHub or AWS, privilege persistence
+has been observed when outdated OAuth tokens or IAM session tokens remain valid after privilege
+revocation. Similarly, in MCP ecosystems, if a revoked API key or modified role configuration is
+not promptly invalidated after an update, an attacker could continue invoking privileged actions,
+potentially compromising the integrity of the system. Enforcing strict privilege revocation policies,
+ensuring privilege changes propagate consistently across all server instances, and implementing
+automatic expiration for API keys and session tokens are essential to reducing the likelihood
+of privilege persistence. Comprehensive logging and auditing of privilege modifications further
+enhance visibility and help detect inconsistencies that could indicate privilege persistence.
+5.3.2 Re-deployment of Vulnerable Versions. MCP servers, being open-source and maintained by
+individual developers or community contributors , lack a centralized platform for auditing
+and enforcing security updates. Users typically download MCP server packages from repositories
+like GitHub, npm, or PyPi and configure them locally, often without formal review processes.
+This decentralized model increases the risk of re-deploying vulnerable versions, either due to
+delayed updates, version rollbacks, or reliance on unverified package sources. When users update
+MCP servers, they may unintentionally roll back to older, vulnerable versions to address compat-
+ibility issues or maintain stability. Additionally, unofficial auto-installers, such as mcp-get and
+mcp-installer , which streamline server installation, may default to cached or outdated versions,
+exposing systems to previously patched vulnerabilities. Since these tools often prioritize ease of
+use over security , they may lack version verification or fail to notify users about critical updates.
+Because security patches in the MCP ecosystem rely on community-driven maintenance, delays
+between vulnerability disclosure and patch availability are common . Users who do not
+actively track updates or security advisories may unknowingly continue using vulnerable versions,
+creating opportunities for attackers to exploit known flaws. For example, an attacker could exploit
+an outdated MCP server to gain unauthorized access or manipulate server operations. From a
+research perspective, analyzing version management practices in MCP environments can identify
+potential gaps and highlight the need for automated vulnerability detection and mitigation. On the
+other hand, there is also a pressing need to establish an official package management system
+with a standardized packaging format for MCP servers and a centralized server registry to
+facilitate secure discovery and verification of available MCP servers.
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 14 ---
+14 X Hou, Y Zhao, S Wang, and H Wang
+5.3.3 Configuration Drift. Configuration drift occurs when unintended changes accumulate in
+the system configuration over time, deviating from the original security baseline. These devia-
+tions often arise due to manual adjustments, overlooked updates, or conflicting modifications
+made by different tools or users. In MCP environments, where servers are typically configured
+and maintained locally by end-users, such inconsistencies can introduce exploitable gaps and
+undermine the overall security posture. With the emergence of remote MCP server support, such
+as Cloudflare’s hosted MCP environments, configuration drift becomes an even more pressing
+concern. Unlike local MCP deployments, where configuration issues may only affect a single user’s
+environment, configuration drift in remote or cloud-based MCP servers can impact multiple users
+or organizations simultaneously. Misconfigurations in multi-tenant environments may expose
+sensitive data, lead to privilege escalation, or inadvertently grant malicious actors broader access
+than intended. Addressing this issue requires the implementation of automated configuration
+validation mechanisms and regular consistency checks to ensure that both local and remote MCP
+environments adhere to secure baseline configurations.
+6 DISCUSSION
+6.1 Implications
+The rapid adoption of MCP is transforming the AI application ecosystem, introducing new oppor-
+tunities and challenges that have significant implications for developers, users, MCP ecosystem
+maintainers, and the broader AI community.
+For developers , MCP reduces the complexity of integrating external tools, enabling the creation
+of more versatile and capable AI agents that can perform complex, multi-step tasks. By providing a
+standardized interface for invoking tools, MCP shifts the focus from managing intricate integrations
+to enhancing agent logic and functionality. However, this increased efficiency comes with the
+responsibility to ensure that MCP implementations are secure, version-controlled, and aligned with
+best practices. Developers must remain vigilant about maintaining secure tool configurations and
+preventing potential misconfigurations that could expose systems to vulnerabilities.
+For users , MCP enhances the experience by enabling seamless interactions between AI agents and
+external tools, automating workflows across platforms such as enterprise data management and IoT
+integration. It reduces the need for manual operations and improves efficiency in handling complex
+tasks. However, as MCP servers gain deeper access to sensitive data and critical operations, users
+must remain vigilant about the risks posed by unverified tools and misconfigured servers. Careless
+installation or untrusted sources may cause data leaks, unauthorized actions, or system instability.
+For MCP ecosystem maintainers , the decentralized nature of MCP server development and
+distribution introduces a fragmented security landscape. MCP servers are often hosted on open-
+source platforms, where updates and patches are community-driven and may vary in quality and
+frequency. Without centralized oversight, inconsistencies in server configurations and outdated
+versions can introduce potential vulnerabilities. As the MCP ecosystem evolves to support remote
+hosting and multi-tenant environments, maintainers must remain attentive to potential risks
+associated with configuration drift, privilege persistence, and re-deployment of vulnerable versions.
+For the broader AI community , MCP unlocks new possibilities by enhancing agentic workflows
+through cross-system coordination, dynamic tool invocation, and collaborative multi-agent systems.
+MCP’s ability to standardize interactions between agents and tools has the potential to accelerate AI
+adoption across industries, driving innovation in fields such as healthcare, finance, and enterprise
+automation. However, as MCP adoption grows, the AI community must address emerging ethical
+and operational concerns, such as ensuring fair and unbiased tool selection, safeguarding sensitive
+user data, and preventing potential misuse of AI capabilities. Balancing these considerations will be
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 15 ---
+Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 15
+essential to ensuring that MCP’s benefits are widely distributed while maintaining accountability
+and trust within the AI ecosystem.
+6.2 Challenges
+Despite its potential, MCP’s adoption brings forth a range of challenges that need to be addressed
+to ensure its sustainable growth and responsible development:
+Lack of centralized security oversight. Since MCP servers are managed by independent develop-
+ers and contributors, there is no centralized platform to audit, enforce, or validate security standards.
+This decentralized model increases the likelihood of inconsistencies in security practices, making it
+difficult to ensure that all MCP servers adhere to secure development principles. Moreover, the
+absence of a unified package management system for MCP servers complicates the installation and
+maintenance process, increasing the likelihood of deploying outdated or misconfigured versions.
+The use of unofficial installation tools across different MCP clients further introduces variability in
+server deployment, making it harder to maintain consistent security standards.
+Authentication and authorization gaps. MCP currently lacks a standardized framework for
+managing authentication and authorization across different clients and servers. Without a unified
+mechanism to verify identities and regulate access, it becomes difficult to enforce granular permis-
+sions, especially in multi-tenant environments where multiple users and agents may interact with
+the same MCP server. The absence of robust authentication protocols increases the risk of unautho-
+rized tool invocation and exposes sensitive data to malicious actors. Moreover, inconsistencies in
+how different MCP clients handle user credentials further exacerbate these security challenges,
+making it difficult to maintain a consistent access control policy across deployments.
+Insufficient debugging and monitoring mechanisms. MCP lacks comprehensive debugging
+and monitoring mechanisms, making it difficult for developers to diagnose errors, trace tool
+interactions, and assess system behavior during tool invocation. Since MCP clients and servers
+operate independently, inconsistencies in error handling and logging can obscure critical security
+events or operational failures. Without robust monitoring frameworks and standardized logging
+mechanisms, identifying anomalies, preventing system failures, and mitigating potential security
+incidents becomes challenging, hindering the development of more resilient MCP ecosystems.
+Maintaining consistency in multi-step, cross-system workflows. MCP allows AI agents to
+execute multi-step workflows by invoking multiple tools across different systems through a unified
+interface. Ensuring consistent context across successive tool interactions is inherently difficult
+due to the distributed nature of these systems. Without effective state management and error
+recovery mechanisms, MCP risks propagating errors or losing intermediate results, leading to
+incomplete or inconsistent workflows. Additionally, dynamic coordination across diverse platforms
+can introduce delays and conflicts, further complicating the seamless execution of workflows within
+MCP environments.
+Scalability challenges in multi-tenant environments. As MCP evolves to support remote server
+hosting and multi-tenant environments, maintaining consistent performance, security, and tenant
+isolation becomes increasingly complex. Without robust mechanisms for resource management
+and tenant-specific configuration policies, misconfigurations can lead to data leakage, performance
+issues, and privilege escalation. Ensuring scalability and isolation is critical for MCP’s reliability in
+enterprise deployments.
+Challenges in embedding MCP in smart environments. Integrating MCP into smart environ-
+ments, such as smart homes, industrial IoT systems, or enterprise automation platforms, introduces
+unique challenges related to real-time responsiveness, interoperability, and security. MCP servers
+in these environments must handle continuous streams of data from multiple sensors and devices
+while maintaining low-latency responses. Moreover, ensuring seamless interaction between AI
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 16 ---
+16 X Hou, Y Zhao, S Wang, and H Wang
+agents and heterogeneous device ecosystems often requires custom adaptations, increasing devel-
+opment complexity. Compromised MCP servers in smart environments can lead to unauthorized
+control over critical systems, threatening both safety and data integrity.
+6.3 Recommendations for MCP stakeholders
+To safeguard the long-term success and security of MCP, all stakeholders, including MCP main-
+tainers, developers, researchers, and end-users, should implement best practices and proactively
+address evolving challenges within the ecosystem.
+Recommendations for MCP maintainers. MCP maintainers play a critical role in establishing
+security standards, improving version control, and ensuring ecosystem stability. To reduce the
+risk of security vulnerabilities, maintainers should establish a formal package management system
+that enforces strict version control and ensures that only verified updates are distributed to users.
+Additionally, introducing a centralized server registry would enable users to discover and validate
+MCP servers more securely, reducing the risk of interacting with malicious or misconfigured
+servers. To further enhance security, maintainers should promote the adoption of cryptographic
+signatures for verifying MCP packages and encourage periodic security audits to identify and
+mitigate vulnerabilities. Moreover, implementing a secure sandboxing framework can help prevent
+privilege escalation and protect host environments from malicious tool executions.
+Recommendations for developers. Developers integrating MCP into AI applications should
+prioritize security and resilience by adhering to secure coding practices and maintaining thorough
+documentation. Enforcing version management policies can prevent rollbacks to vulnerable versions,
+while thorough testing ensures reliable MCP integrations before deployment. To mitigate configura-
+tion drift, developers should automate configuration management and adopt infrastructure-as-code
+(IaC) practices. Additionally, implementing robust tool name validation and disambiguation tech-
+niques can prevent conflicts that lead to unintended behavior. Leveraging runtime monitoring and
+logging helps track tool invocations, detect anomalies, and mitigate threats effectively.
+Recommendations for researchers. Given the decentralized nature of MCP server deployment
+and the evolving threat landscape, researchers should focus on conducting systematic security
+analyses to uncover potential vulnerabilities in tool invocation, sandbox implementations, and
+privilege management. Exploring techniques to enhance sandbox security, mitigate privilege
+persistence, and prevent configuration drift can significantly strengthen MCP’s security posture. In
+addition, researchers should investigate more effective approaches for version control and package
+management in decentralized ecosystems to reduce the likelihood of re-deploying vulnerable
+versions. Researchers can help MCP maintainers and developers stay ahead of emerging threats
+by developing automated vulnerability detection methods and proposing secure update pipelines.
+Another critical area for research is the exploration of context-aware agent orchestration in multi-
+tool environments. As MCP increasingly supports multi-step, cross-system workflows, ensuring
+state consistency and preventing tool invocation conflicts becomes paramount. Researchers can
+explore techniques for dynamic state management, error recovery, and workflow validation to
+ensure seamless operation in complex environments.
+Recommendations for end-users. End-users should remain vigilant about security risks and
+adopt practices to safeguard their environments. They should prioritize using verified MCP servers
+and avoid unofficial installers that may introduce vulnerabilities. Regularly updating MCP servers
+and monitoring configuration changes can prevent misconfigurations and reduce exposure to
+known exploits. Properly configuring access control policies helps prevent privilege escalation
+and unauthorized tool usage. For users relying on remote MCP servers, choosing providers that
+follow strict security standards can minimize risks in multi-tenant environments. Promoting user
+awareness and encouraging best practices will enhance overall security and resilience.
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 17 ---
+Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 17
+7 RELATED WORK
+7.1 Tool Integration in LLM Applications
+Equipping LLMs with external tools has become a key paradigm for enhancing their capabilities
+in real-world tasks. This approach enables LLMs to transcend the limitations of static knowledge
+and interact dynamically with external systems. Recent studies have proposed frameworks to
+support such integration, focusing on tool representation, selection, invocation, and reasoning.
+Shen et al.[ 44] provide a comprehensive survey outlining a standard LLM-tool integration paradigm,
+identifying key challenges in user intent understanding, tool selection, and execution planning.
+Building on this, AutoTools[45, 46] introduces an automated framework that transforms raw tool
+documentation into executable functions, reducing reliance on manual engineering. EasyTool [ 59]
+further streamlines this process by distilling diverse and verbose tool documentation into concise
+and unified instructions, improving tool usability and efficiency. From an evaluation perspective,
+several benchmarks have emerged. ToolSandbox [ 30] emphasizes stateful and interactive tool usage
+with implicit dependencies, while UltraTool [ 23] focuses on complex, multi-step tasks involving
+planning, creation, and execution. These efforts reveal significant performance gaps and motivate
+better evaluations for LLM-agent capabilities. To improve agent decision-making and prompt
+quality, AvaTaR [ 54] proposes contrastive reasoning techniques, while Toolken+[ 56] incorporates
+reranking and rejection mechanisms for more precise tool use. Additionally, some works explore
+LLMs not just as tool users but as tool creators—ToolMaker[ 53] autonomously converts code
+repositories into callable tools, moving toward fully automated agents. To unify this expanding
+landscape, Li [ 27] proposes a taxonomy that situates tool use alongside planning and feedback
+learning as three core agent paradigms.
+As tool-augmented LLMs continue to evolve, the lack of a standardized, secure, and extensible
+context protocol has become a key bottleneck. MCP, with its potential to unify tool interaction across
+diverse systems, is poised to become the foundational layer for next-generation LLM applications,
+making it critical to examine its landscape, limitations, and risks.
+7.2 Security Risks in LLM-Tool Interactions
+The integration of tool-use capabilities into LLM agents significantly expands their functionality,
+but also introduces new and more severe security risks. Fu et al. [ 17] demonstrate that obfuscated
+adversarial prompts can lead LLM agents to misuse tools, enabling attacks such as data exfiltration
+and unauthorized command execution. These vulnerabilities are particularly concerning as they
+generalize across models and modalities. A growing body of work has begun to categorize and
+analyze these risks. Gan et al.[ 18] and Yu et al.[ 58] propose taxonomies for threats across agent
+components and stages, while the OWASP Agentic Security Initiative [ 25] provides practical threat
+modeling frameworks. To support detection and mitigation, Chen et al.[ 5] introduce AgentGuard,
+which automatically discovers unsafe workflows and generates safety constraints, and ToolFuzz[ 36]
+identifies failures stemming from ambiguous or underspecified tool documentation. On the align-
+ment front, Chen et al.[ 6] propose the H2A principle, which encourages LLMs to behave with
+helpfulness, harmlessness, and autonomy, and introduce the ToolAlign dataset to guide safer tool
+usage. Ye et al.[ 57] further analyze safety risks throughout the tool-use pipeline, including malicious
+queries, execution misdirection, and unsafe outputs. Deng et al. [ 13] highlight broader systemic
+risks such as unpredictable inputs, environmental variability, and untrusted tool endpoints.
+These security threats may be mitigated through the structured design of MCP, but they can also
+persist or even evolve under this new integration paradigm. As MCP simplifies tool orchestration
+in LLM applications, it simultaneously introduces new potential attack surfaces, warranting deeper
+investigation into its security implications.
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 18 ---
+18 X Hou, Y Zhao, S Wang, and H Wang
+8 CONCLUSION
+This paper presents the first comprehensive analysis of the MCP ecosystem landscape. We examine
+its architecture, core components, operational workflows, and server lifecycle stages. Furthermore,
+we explore the adoption, diversity, and use cases, while identifying potential security threats
+throughout the creation, operation, and update phases. We also highlight the implications and
+risks associated with MCP adoption and propose actionable recommendations for stakeholders
+to enhance security and governance. Additionally, we outline future research directions to tackle
+emerging risks and improve MCP’s resilience. As MCP continues to gain traction with industry
+leaders such as OpenAI and Cloudflare, addressing these challenges is key to its long-term success
+and to enabling secure, efficient interaction with diverse external tools and services.
+REFERENCES
+[1]ahujasid. 2025. BlenderMCP - Blender Model Context Protocol Integration. https://github.com/ahujasid/blender-mcp.
+[2] Anthropic. 2024. For Claude Desktop Users. https://modelcontextprotocol.io/quickstart/user.
+[3]Anthropic. 2024. Introducing the Model Context Protocol. https://www.anthropic.com/news/model-context-protocol.
+[4] ByteDance. 2024. Coze plugin store. https://www.coze.com/store/plugin.
+[5]Jizhou Chen and Samuel Lee Cong. 2025. AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of
+Tool Orchestration. CoRR abs/2502.09809 (2025). https://doi.org/10.48550/ARXIV.2502.09809 arXiv:2502.09809
+[6]Zhi-Yuan Chen, Shiqi Shen, Guangyao Shen, Gong Zhi, Xu Chen, and Yankai Lin. 2024. Towards Tool Use Alignment
+of Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing .
+1382–1400.
+[7] Cline. 2025. Cline. https://github.com/cline/cline.
+[8] Cloudflare. 2025. Cloudflare. https://www.cloudflare.com.
+[9] Codeium. 2025. Codeium. https://codeium.com.
+[10] Sourcegraph Cody. 2025. Cody supports additional context through Anthropic’s Model Context Protocol. https:
+//sourcegraph.com/blog/cody-supports-anthropic-model-context-protocol.
+[11] Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola
+Tonellotto, and Fabrizio Silvestri. 2024. The Power of Noise: Redefining Retrieval for RAG Systems. CoRR abs/2401.14887
+(2024). https://doi.org/10.48550/ARXIV.2401.14887 arXiv:2401.14887
+[12] Cursor. 2025. Learn how to add and use custom MCP tools within Cursor. https://docs.cursor.com/context/model-
+context-protocol.
+[13] Zehang Deng, Yongjian Guo, Changzhou Han, Wanlun Ma, Junwu Xiong, Sheng Wen, and Yang Xiang. 2024. AI
+Agents Under Threat: A Survey of Key Security Challenges and Future Pathways. CoRR abs/2406.02630 (2024).
+https://doi.org/10.48550/ARXIV.2406.02630 arXiv:2406.02630
+[14] Windsurf Editor. 2025. Windsurf Editor. https://windsurf.com.
+[15] Antanavicius et al. 2025. PulseMCP. https://www.pulsemcp.com.
+[16] Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li. 2024. A
+Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models. In Proceedings of the 30th ACM
+SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain, August 25-29, 2024 , Ricardo
+Baeza-Yates and Francesco Bonchi (Eds.). ACM, 6491–6501. https://doi.org/10.1145/3637528.3671470
+[17] Xiaohan Fu, Shuheng Li, Zihan Wang, Yihao Liu, Rajesh K. Gupta, Taylor Berg-Kirkpatrick, and Earlence Fernandes.
+2024. Imprompter: Tricking LLM Agents into Improper Tool Use. CoRR abs/2410.14923 (2024). https://doi.org/10.
+48550/ARXIV.2410.14923 arXiv:2410.14923
+[18] Yuyou Gan, Yong Yang, Zhe Ma, Ping He, Rui Zeng, Yiming Wang, Qingming Li, Chunyi Zhou, Songze Li, Ting Wang,
+Yunjun Gao, Yingcai Wu, and Shouling Ji. 2024. Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats
+in LLM-Based Agents. CoRR abs/2411.09523 (2024). https://doi.org/10.48550/ARXIV.2411.09523 arXiv:2411.09523
+[19] gching. 2025. Toolbase. https://gettoolbase.ai.
+[20] glama.ai. 2025. Glama MCP Servers. https://glama.ai/mcp/servers.
+[21] Goose. 2025. Goose. https://goose.ai.
+[22] Nadeeshaan Gunasinghe and Nipuna Marcus. 2021. Language Server Protocol and Implementation . Springer.
+[23] Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng
+Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, and Qun Liu. 2024. Planning, Creation, Usage: Benchmarking LLMs for
+Comprehensive Tool Utilization in Real-World Complex Scenarios. CoRR abs/2401.17167 (2024). https://doi.org/10.
+48550/ARXIV.2401.17167 arXiv:2401.17167
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 19 ---
+Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions 19
+[24] JetBrains. 2025. JetBrains MCP Server. https://plugins.jetbrains.com/plugin/26071-mcp-server.
+[25] Sotiropoulos John, Rosario Ron F Del, Kokuykin Evgeniy, Oakley Helen, Habler Idan, Underkoffler Kayla, Huang Ken,
+Steffensen Peter, Aralimatti Rakshith, Bitton Ron, et al .2025. OWASP Top 10 for LLM Apps & Gen AI Agentic Security
+Initiative . Ph. D. Dissertation. OWASP.
+[26] LangChain. 2022. LangChain: Framework for developing applications powered by language models. https://github.
+com/langchain-ai/langchain.
+[27] Xinzhe Li. 2025. A Review of Prominent Paradigms for LLM-Based Agents: Tool Use, Planning (Including RAG),
+and Feedback Learning. In Proceedings of the 31st International Conference on Computational Linguistics, COLING
+2025, Abu Dhabi, UAE, January 19-24, 2025 , Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa,
+Barbara Di Eugenio, and Steven Schockaert (Eds.). Association for Computational Linguistics, 9760–9779. https:
+//aclanthology.org/2025.coling-main.652/
+[28] LibreChat. 2025. LibreChat. https://librechat.ai.
+[29] Jerry Liu. 2022. LlamaIndex: A data framework for LLM applications. https://github.com/run-llama/llama_index.
+[30] Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Felix Bai, Shuang Ma, Shen Ma, Mengyu Li,
+Guoli Yin, Zirui Wang, and Ruoming Pang. 2024. ToolSandbox: A Stateful, Conversational, Interactive Evaluation
+Benchmark for LLM Tool Use Capabilities. CoRR abs/2408.04682 (2024). https://doi.org/10.48550/ARXIV.2408.04682
+arXiv:2408.04682
+[31] Baidu Maps. 2025. Baidu Maps MCP Servers. https://lbs.baidu.com/faq/api?title=mcpserver/base.
+[32] Emacs MCP. 2025. Emacs MCP. https://github.com/lizqwerscott/mcp.el.
+[33] Tripo3D MCP. 2025. Tripo3D MCP. https://blender-mcp.com/.
+[34] mcp dockmater. 2025. Dockmaster. https://mcp-dockmaster.com.
+[35] mcp.so. 2025. MCP.so. https://mcp.so/.
+[36] Ivan Milev, Mislav Balunović, Maximilian Baader, and Martin Vechev. 2025. ToolFuzz–Automated Agent Tool Testing.
+arXiv preprint arXiv:2503.04479 (2025).
+[37] OpenAI. 2023. ChatGPT plugins. https://openai.com/index/chatgpt-plugins/.
+[38] OpenAI. 2023. Funcation Calling. https://platform.openai.com/docs/guides/function-calling?api-mode=responses.
+[39] OpenAI. 2025. OpenAI Agents SDK - Model context protocol (MCP). https://openai.github.io/openai-agents-python/
+mcp/.
+[40] OpenSumi. 2025. OpenSumi. https://github.com/opensumi/core.
+[41] Model Context Protocol. 2024. GitHub MCP Server. https://github.com/modelcontextprotocol/servers/tree/main/src/
+github.
+[42] Model Context Protocol. 2024. Slack MCP Server. https://github.com/modelcontextprotocol/servers/tree/main/src/
+slack.
+[43] Replit. 2025. Replit. https://replit.com.
+[44] Zhuocheng Shen. 2024. LLM With Tools: A Survey. CoRR abs/2409.18807 (2024). https://doi.org/10.48550/ARXIV.2409.
+18807 arXiv:2409.18807
+[45] Zhengliang Shi, Shen Gao, Xiuyi Chen, Yue Feng, Lingyong Yan, Haibo Shi, Dawei Yin, Zhumin Chen, Suzan Ver-
+berne, and Zhaochun Ren. 2024. Chain of Tools: Large Language Model is an Automatic Multi-tool Learner. CoRR
+abs/2405.16533 (2024). https://doi.org/10.48550/ARXIV.2405.16533 arXiv:2405.16533
+[46] Zhengliang Shi, Shen Gao, Lingyong Yan, Yue Feng, Xiuyi Chen, Zhumin Chen, Dawei Yin, Suzan Verberne, and
+Zhaochun Ren. 2025. Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents. In THE
+WEB CONFERENCE 2025 . https://openreview.net/forum?id=T4wMdeFEjX
+[47] Block (Square). 2025. Block (Square). https://glama.ai/mcp/servers/atblock/square-mcp/tools/team.
+[48] Stripe. 2025. Stripe. https://stripe.com.
+[49] Microsoft Copilot Studio. 2025. Introducing Model Context Protocol (MCP) in Copilot Studio: Simplified Integration
+with AI Apps and Agents. https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/introducing-model-
+context-protocol-mcp-in-copilot-studio-simplified-integration-with-ai-apps-and-agents/.
+[50] Tencent. 2024. Tencent plugin shop. https://yuanqi.tencent.com/plugin-shop.
+[51] Apify MCP Tester. 2025. Apify MCP Tester. https://apify.com/jiri.spilka/tester-mcp-client.
+[52] TheiaAI/TheiaIDE. 2025. TheiaAI/TheiaIDE. https://theia-ide.org/docs/user_ai/.
+[53] Georg Wölflein, Dyke Ferber, Daniel Truhn, Ognjen Arandjelovic, and Jakob Nikolas Kather. 2025. LLM Agents Making
+Agent Tools. CoRR abs/2502.11705 (2025). https://doi.org/10.48550/ARXIV.2502.11705 arXiv:2502.11705
+[54] Shirley Wu, Shiyu Zhao, Qian Huang, Kexin Huang, Michihiro Yasunaga, Kaidi Cao, Vassilis N. Ioannidis, Karthik
+Subbian, Jure Leskovec, and James Y. Zou. 2024. AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive
+Reasoning. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing
+Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024 , Amir Globersons, Lester Mackey, Danielle
+Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, and Cheng Zhang (Eds.). http://papers.nips.cc/paper_files/
+, Vol. 1, No. 1, Article . Publication date: April 2025.
+
+--- Page 20 ---
+20 X Hou, Y Zhao, S Wang, and H Wang
+paper/2024/hash/2db8ce969b000fe0b3fb172490c33ce8-Abstract-Conference.html
+[55] Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin,
+Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng
+Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, Wensen Cheng, Qi Zhang, Wenjuan Qin, Yongyan
+Zheng, Xipeng Qiu, Xuanjing Huang, and Tao Gui. 2023. The Rise and Potential of Large Language Model Based
+Agents: A Survey. CoRR abs/2309.07864 (2023). https://doi.org/10.48550/ARXIV.2309.07864 arXiv:2309.07864
+[56] Konstantin Yakovlev, Sergey I. Nikolenko, and Andrey Bout. 2024. Toolken+: Improving LLM Tool Usage with Reranking
+and a Reject Option. CoRR abs/2410.12004 (2024). https://doi.org/10.48550/ARXIV.2410.12004 arXiv:2410.12004
+[57] Junjie Ye, Sixian Li, Guanyu Li, Caishuang Huang, Songyang Gao, Yilong Wu, Qi Zhang, Tao Gui, and Xuanjing Huang.
+2024. ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages. CoRR
+abs/2402.10753 (2024). https://doi.org/10.48550/ARXIV.2402.10753 arXiv:2402.10753
+[58] Miao Yu, Fanci Meng, Xinyun Zhou, Shilong Wang, Junyuan Mao, Linsey Pang, Tianlong Chen, Kun Wang, Xinfeng
+Li, Yongfeng Zhang, et al .2025. A Survey on Trustworthy LLM Agents: Threats and Countermeasures. arXiv preprint
+arXiv:2503.09648 (2025).
+[59] Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Kan Ren, Dongsheng Li, and Deqing Yang. 2024.
+EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction. CoRR abs/2401.06201 (2024). https:
+//doi.org/10.48550/ARXIV.2401.06201 arXiv:2401.06201
+[60] Zed. 2025. Zed - Model Context Protocol. https://zed.dev/docs/assistant/model-context-protocol.
+[61] Mingming Zha, Jice Wang, Yuhong Nan, Xiaofeng Wang, Yuqing Zhang, and Zelin Yang. 2022. Hazard Integrated:
+Understanding Security Risks in App Extensions to Team Chat Systems. In 29th Annual Network and Distributed System
+Security Symposium, NDSS 2022, San Diego, California, USA, April 24-28, 2022 . The Internet Society. https://www.ndss-
+symposium.org/ndss-paper/auto-draft-262/
+[62] Yanjie Zhao, Xinyi Hou, Shenao Wang, and Haoyu Wang. 2024. LLM App Store Analysis: A Vision and Roadmap.
+CoRR abs/2404.12737 (2024). https://doi.org/10.48550/ARXIV.2404.12737 arXiv:2404.12737
+, Vol. 1, No. 1, Article . Publication date: April 2025.

data/documents/content/b3d0767c-1dd8-4f05-a9f8-d69d82f2abde.txt

@@ -0,0 +1,1137 @@
+--- Page 1 ---
+A Study on the MCP × A2A Framework for Enhancing 
+Interoperability of LLM -based Autonomous Agents  
+ Cheonsu Jeong  
+Hyper Automation Team, SAMSUNG SDS, Seoul , 05510 , South Korea  
+   
+ 
+I. INTRODUCTION  
+ECENT  advancements in artificial intelligence (AI) have highlighted 
+the importance of large language models (LLMs) across various 
+domains [1]. Generative AI, which can create new content such as text, 
+images, audio, and video based on extensive training data, en ables 
+users to leverage AI services with ease [1],[2]. In particular, the 
+development of AI has led to a growing utilization of autonomous 
+agents —AI systems capable of independently performing tasks, 
+solving problems, and supporting decision -making based o n user 
+inputs. However, complex real -world problems often exceed the 
+capabilities of a single agent. This has given rise to the necessity of 
+multi -agent systems (MAS), where multiple agents collaborate to 
+overcome such limitations. With the emergence of LL Ms, LLM -based 
+MASs have gained attention as a new paradigm [4]. Unlike traditional 
+MASs, these systems enable more flexible and adaptive collaboration 
+through natural language processing. Each agent can specialize in a 
+specific domain while coordinating co mplex tasks through natural 
+language interaction [5]. 
+For agents to be effectively utilized in real -world environments, 
+seamless integration with external tools, APIs, and databa ses is 
+critical. To address this, Anthropic released the Model Context 
+Protocol (MCP) as open source in November 2024 [6]. Similarly, 
+efficient communication and collaboration among agents is a 
+prerequisite for successful MAS deployment. However, agent sys tems 
+developed by different frameworks and vendors have faced 
+interoperability limitations. To resolve this, Google released the 
+Agent -to-Agent (A2A) protocol as an open source project in April 2025 
+[7]. A2A provides a standardized communication framework for 
+agents to collaborate across different environments, while MCP 
+offers a structured I/O system that enables agents to interact with external tools and resources [8]. Although these protocols function 
+independently, they serve complementary roles and for m a 
+foundational basis for building practical agent ecosystems.  
+This paper analyzes the technical architecture and operational 
+principles of A2A and MCP, exploring how they synergistically 
+contribute to constructing scalable and interoperable agent 
+ecosystems. By presenting an integrated architecture using 
+LangGraph an d detailing key implementation logic, we demonstrate 
+how theoretical concepts are translated into real -world applications. 
+Through this combined use of A2A and MCP, we propose a practical 
+approach to enhancing interoperability and development efficiency 
+of LLM-based agent systems across various enterprise environments, 
+thereby unlocking the potential to transition agent technologies from 
+experimental stages to fully deployed services.  
+II. RELATED WORK 
+A. Concept and Evolution of Autonomous Agents  
+An automation age nt refers to more than just a task executor —it 
+is an intelligent software robot that integrates AI technologies to 
+perceive complex situations, analyze data, interact with users, and 
+even improve its performance through learning [9]. Autonomous 
+agents are systems that perceive their environment, autonomously 
+set goals or formulate plans to achieve predefined objectives, and 
+make decisions and act independently by learning and adapting [10], 
+[11]. These agents can respond flexibly to unpredictable or dynamic  
+environments, solving problems on their own as needed. The recent 
+advancement of large language models (LLMs) has significantly 
+expanded the capabilities and application areas of autonomous 
+agents. They are now widely used in information retrieval, data 
+analysis, decision support, and task automation, and are increasingly 
+being adopted as productivity tools in enterprise environments.  
+Autonomous agents possess several key characteristics, as 
+summarized in Table 1:  R ABSTRACT  
+This paper provides an in -depth technical analysis and implementation methodology of the open -source 
+Agent -to-Agent (A2A) protocol developed by Google and the Model Context Protocol (MCP) introduced by 
+Anthropic. While the evolution of LLM -based autonomous agents is rapidly accelerating, efficient interactions 
+among these agents and their integration with external systems remain significant challenges. In modern AI 
+systems, collaboration between autonomous agents and integration with external tool s have become 
+essential elements for building practical AI applications. A2A offers a standardized communication method 
+that enables agents developed in heterogeneous environments to collaborate effectively, while MCP provides 
+a structured I/O framework fo r agents to connect with external tools and resources. Prior studies have focused 
+primarily on the features and applications of either A2A or MCP individually. In contrast, this study takes an 
+integrated approach, exploring how the two protocols can comple ment each other to address interoperability 
+issues and facilitate efficient collaboration within complex agent ecosystems.  KEYWORDS  
+Agent -to-Agent (A2A), 
+Model Context Protocol 
+(MCP), Multi -Agent 
+System ( MAS), 
+Autonomous Agent, 
+Agent Collaboration.    
+* Corresponding authors:  
+E-mail address: csu.jeong@samsung.com (C . Jeong).  
+
+--- Page 2 ---
+ 
+TABLE  I. KEY CHARACTERISTICS OF AUTONOMOU S AGENTS  
+Item  Description  
+Autonomy  The ability to operate independently without 
+external intervention.  
+Goal -Oriented  The ability to formulate and execute plans to 
+achieve specific objectives.  
+Adaptability  The capability to adjust behavior in response to  
+environmental changes.  
+Social Ability  The ability to interact with other agents or 
+users.  
+ 
+However, due to constraints in knowledge, functionality, and 
+resources, a single agent often faces limitations in solving complex 
+problems. To overcome these limitations, Multi -Agent Systems (MAS) 
+have emerged, where multiple agents collaborate toward a shared 
+goal.  
+ 
+B. Necessity of Multi -Agent Systems (MAS)  
+A MAS is a framework in which multiple autonomous agents 
+collaborate to solve complex problems. Each agent is assigned a 
+specific role and area of expertise, and their cooperation enables the 
+resolution of problems that would be infeasible for a single age nt 
+alone [1]. 
+The main advantages of MAS include:  
+1.  Distributed Problem Solving:  Complex problems are 
+decomposed into sub -problems, with each agent addressing a 
+specific domain.  
+2. Scalability:  System functionality can be expanded by adding or 
+modifying agents.  
+3. Robustness:  System functionality is maintained even if some 
+agents fail.  
+4. Diverse Perspectives:  Agents with different expertise analyze 
+problems from varied angles.  
+ 
+However, effe ctive MAS operation requires seamless 
+communication and collaboration among agents, necessitating 
+standardized communication protocols and collaboration 
+mechanisms.  
+ 
+C. Importance of Agent Communication Protocols  
+Agent communication protocols define how agent s exchange 
+messages, including the format, communication rules, and semantic 
+interpretation. Communication is fundamental to MAS, enabling 
+autonomous entities to coordinate and collaborate in solving complex 
+problems [12].  
+An effective communication proto col must satisfy the following 
+requirements:  
+1.  Interoperability:  Support communication between agents 
+developed on different platforms and frameworks.  
+2. Extensibility:  Accommodate new functions and requirements.  
+3. Security:  Provide mechanisms for secu re communication.  
+4. Efficiency:  Minimize communication overhead and support 
+efficient message exchange.  
+5. Standardization:  Offer standardized formats and rules for 
+broad adoption.   
+Fig. 1 . Integrated Implementation Architecture Concept   
+ 
+D. Technical Analysis of the A2A Protocol  
+1. Concept and Structure of A2A Protocol  
+Only two levels of headings should be numbered. Lower level 
+headings remain unnumbered; they are formatted as run -in headings.  
+The A2A protocol is a standardized communication framework 
+designed to enable effective collaboration among autonomous 
+agents developed in diverse environments. Released as open -source 
+by Google, it abstracts the complexity of agent communication and 
+provides a consistent interface to ensure interoperability [8].  
+ 
+Fig. 2 . Open Standard A2A  Protocol Concept Diagram  [7]  
+ 
+The core structure of the A2A protocol consists of:  
+1. Agent Card:  Metadata in JSON format specifying an agent’s 
+functions, roles, and capabilities, enabling other agents to 
+discover and determine appropriate collaboration methods.  
+2. Message Format:  Defines a structured format for agent 
+communication, including headers, body, and parts, supporting 
+various content types (text, images, structured data).  
+3. Task Management:  Adopts a task -based communi cation model, 
+tracking task status, progress, and outcomes.  
+4. Artifacts:  Standardized data structures representing task 
+outcomes, such as documents, code, or images.  
+The A2A protocol achieves loose coupling and high cohesion, 
+enhancing the flexibility and  scalability of agent systems.  
+2. Core Functions of the A2A Protocol  
+A2A supports a range of core functionalities that enable 
+collaborative operations among agents:  
+
+
+--- Page 3 ---
+ 
+1. Capability Discovery:  Agents advertise their capabilities using 
+an “Agent Card” in JSON for mat. This mechanism functions 
+similarly to service registries in Service -Oriented Architecture 
+(SOA), allowing for dynamic discovery and coordination [8]. 
+Each Agent Card includes essential information such as agent ID, 
+name, description, supported functio ns, input/output formats, 
+authentication requirements, and version information. This 
+mechanism supports dynamic expansion of the agent 
+ecosystem, enabling existing agents to recognize and 
+collaborate with newly added agents automatically.  
+2. Task and State  Management:  A2A structures agent 
+communication around tasks, each with a unique identifier and 
+life cycle states (e.g., created, in -progress, completed, failed). 
+Results are returned as standardized artifacts [8]. This task -
+oriented design supports asynch ronous processing, real -time 
+monitoring, task interruption and resumption, and consistent 
+handling of results. It facilitates coordination in complex 
+collaboration scenarios and provides a foundation for recovery 
+strategies in case of failure.  
+3. Secure Co llaboration:  A2A includes mechanisms to ensure 
+secure communication between agents. It supports enterprise -
+grade authentication and OpenAPI -based authorization, 
+enabling safe exchanges of context, responses, artifacts, and 
+user directives [8], [13], [14]. Key security elements include 
+authentication, authorization, encryption, access control, and 
+audit trails. These features allow for secure inter -agent 
+communication while maintaining data protection, which is 
+essential for enterprise use cases.  
+4. User Exp erience Negotiation:  Each A2A message can include 
+fully composed “parts” that represent complete content 
+blocks —such as formatted text, images, or interactive elements. 
+The protocol supports negotiation for user interface elements, 
+enhancing user experienc e consistency across different clients 
+[8]. This feature provides benefits such as multimodal content 
+support, consistent UX delivery, adaptation to various client 
+environments, and standardized representation of interactive 
+content. It ensures consistent rendering of agent -generated 
+content across interfaces.  
+3. Technical Advantages of the A2A Protocol  
+The A2A protocol provides the following technical advantages:  
+1. Standardized Communication:  Standardizes communication 
+among agents developed in different frameworks and vendors, 
+ensuring interoperability [8], [13]. 
+2. Loose Coupling:  Agents can collaborate without knowledge of 
+each other’s internal implementation, enhancing system 
+flexibility and scalability.  
+3. Dynamic Discovery:  Dynamic capability discovery through 
+agent cards enables easy integration of new agents and 
+functionaliti es. 
+4. Multimodal Communication:  Supports exchange of various 
+types of content, including text, images, and structured data.  
+5. Extensible Design:  JSON -based message format and modular 
+structure allow easy accommodation of new requirements and 
+functionalit ies. 
+6. Enterprise Support:  Provides essential security features such as 
+authentication, authorization, and auditing for enterprise 
+environments.  
+These technical advantages significantly reduce the development 
+and deployment costs of agent systems and impr ove collaboration efficiency among diverse agents.  
+ 
+E. Technical Analysis of the Model Context Protocol (MCP)  
+1. Concept and Structure of MCP  
+The MCP is a standardized protocol designed to enable AI agents 
+to connect effectively with external tools, APIs, and re sources  [14]. As 
+a core component enhancing agent practicality, MCP establishes a 
+structured input/output framework that facilitates seamless 
+interactions between agents and diverse external resources [8]. 
+The primary structural elements of MCP include:  
+1. Context Definition: Specifies the context in which the agent 
+interacts with external tools, including tool functionalities, input 
+parameters, and output formats.  
+2. Schema -Based Interface: Uses JSON Schema to clearly 
+define the input and output types for each tool. This allows the agent 
+to understand the tool’s interface and provide appropriate input.  
+3. Function Calling Mechanism: Provides a standardized 
+mechanism for agents to invoke external tool functions and retrieve 
+their results.  
+4. State Management : Maintains state information during tool 
+usage, supporting complex multi -step interactions.  
+The structure of MCP balances clearly defined boundaries between 
+agents and tools with the flexibility necessary for dynamic interaction.  
+ 
+2. Concept and Structure of  MCP  
+MCP provides various core functions for agents to interact 
+effectively with external tools:  
+1. Tool Discovery and Description:  MCP provides a mechanism to 
+clearly describe tool functionality and usage. This is achieved 
+through JSON schema -based tool d escriptions, which include 
+tool name and description, list of supported functions, input 
+parameters and types, output formats and types, examples, and 
+usage instructions [8]. This tool discovery mechanism enables 
+agents to dynamically discover available to ols and select 
+appropriate tools to perform tasks.  
+2. Function Calling and Result Handling:  MCP provides a 
+standardized way for agents to call functions of external tools 
+and handle results. This process consists of the following steps 
+[8]: 
+─ Function Selection: The agent analyzes the user’s request 
+and selects an appropriate function.  
+─ Parameter Construction: The agent constructs the 
+parameters required for function execution.  
+─ Function Call: The agent calls the function with the 
+constructed paramete rs. 
+─ Result Reception: The agent receives the result of the 
+function execution.  
+─ Result Interpretation: The agent interprets the received 
+result and provides it to the user or uses it for subsequent 
+tasks.  
+This function calling mechanism enables agents t o utilize 
+various external tools an d APIs to perform actual tasks.  
+
+--- Page 4 ---
+ 
+3. Context Management:  MCP provides functions to effectively 
+manage context during interaction between agents and tools. 
+This includes session management, state tracking, context 
+propagation, and memory management [8]. These context 
+management functions enable agents to maintain consistent 
+state and interact with tools during complex multi -step tasks.  
+4. Error Handling and Recovery:  MCP provides mechanisms to 
+handle and recover from errors that may occur during tool 
+usage. This includes standardization of error codes and 
+messages, retry strategies, fallback paths, and graceful 
+degradation [8]. These error handling mechanisms enable 
+agents to operate robustly even in unexpected situations.  
+3. Technical Advantages of MCP  
+MCP provides the following technical advantages:  
+1. Stand ardized Tool Integration:  Enables consistent integration 
+of various tools and APIs, making it easy to extend agent 
+functionality.  
+2. Type Safety:  JSON schema -based interfaces ensure type safety, 
+reducing errors in agent -tool interaction.  
+3. Self-documentin g: Tool descriptions are included as part of the 
+protocol, allowing tool usage to be understood without 
+separate documentation.  4. Extensible Design:  Provides an extensible structure for easily 
+adding new tools and functionalities.  
+5. Language and Platform  Independence:  Adopts an open 
+standard that can be implemented in various programming 
+languages and platforms.  
+6. Developer -friendly Interface:  Provides an intuitive interface 
+that is easy for developers to understand and implement.  
+These technical advanta ges significantly expand the practical 
+utility and scope of agents and make it easier for developers to build 
+and extend agent systems.  
+ 
+F. Comparison of MCP, A2A, and API  
+The main characteristics  of MCP , A2A, and general APIs are 
+compared in Table 2.  
+This co mparison table shows the main characteristics and 
+differences of each protocol/interface. MCP focuses on enabling 
+agents to interact effectively with external tools, A2A facilitates 
+collaboration among agents, and general APIs provide a standard 
+interface for data exchange between services. These three 
+approaches are complementary and, when used together, can build a TABLE 2. COMPARISON OF MCP,  A2A,  AND GENERAL API 
+Feature  MCP  A2A  General API  
+Primary Purpose  Connecting agents to external 
+tools/services  Communication and 
+collaboration between agents  Data exchange between client 
+and server  
+Communication 
+Direction  Agent → Tool  Agent ↔ Agent  Client ↔ Server  
+Message Format  JSON Schema -based  Structured messages (Header, 
+Body, Parts)  Varies: REST, SOAP, etc.  
+State Management  Context -based state tracking  Task -based state management  Stateless or session -based  
+Discovery Mechanism  Tool Descriptions  Agent Cards  API documentation, OpenAPI 
+specs  
+Authentication Method  Supports various mechanisms  Enterprise authentication, 
+OpenAPI -based  Varies: API Key, OAuth, etc. 
+Error Handling  Standardized error codes and 
+recovery strategies  Task failure handling and 
+recovery  HTTP status codes, error 
+responses  
+Scalability  Easy integration of new tools  Easy integration of new agents  Scalable through endpoint 
+expansion  
+Implementation 
+Complexity  Moderate  High  Low to moderate  
+Multimodal Support  Limited  Comprehensive (Text, Image, 
+Structured Data)  Limited  
+Level of Standardization  Emerging standard  Emerging standard  Widely varied  
+Developer Experience  Self-descriptive, schema -based  Discovery via Agent Cards  Highly documentation -
+dependent  
+Real -time Interaction  Limited  Supported  Depends on API design  
+Security Model  Tool -specific access control  Inter -agent authentication and 
+authorization  Endpoint -specific access control  
+Typical Use Cases  Tool/API connection, automation 
+via function calls  Collaborative problem solving, 
+long -running coordination  Information retrieval, command 
+dispatch  
+ 
+
+--- Page 5 ---
+ 
+powerful agent eco system.  
+ 
+III. MCP –A2A  INTEGRATED IMPLEMENTATION METHODOLOGY  
+A. Implementation Architecture Overview  
+To implement MCP and A2A in real systems, a systematic 
+architectural design is required. This section explains the architectural 
+components and design principles for effectively  implementing both 
+protocols.  
+The integrated implementation architecture of MCP and A2A 
+consists of the following main layers  (Fig. 2) : 
+1. User Interface Layer:  Responsible for interaction between users 
+and the agent system, handling user re quests and displaying 
+results. This layer provides a consistent user experience across 
+diverse client environments and effectively represents 
+multimodal content using A2A’s user experience negotiation 
+features.  
+2. Agent Management Layer:  Manages the lifecy cle, state, and 
+functionality of agents, including creation and management of 
+agent cards and coordination of agent communication. 
+According to Habler et al. [13], this layer creates a flexible 
+collaboration environment through dynamic agent discovery 
+and capability negotiation.  
+3. Core Protocol Layer:  Implements the basic protocol 
+specifications of A2A and MCP, providing core functions such as 
+message formats, communication rules, and error handling. 
+This layer ensures protocol compliance and enables consi stent 
+implementation across diverse environments.  
+4. Tool Integration Layer:  Integrates external tools and APIs 
+through MCP, managing tool descriptions and processing function calls. According to the latest specification of the MCP 
+[14], this layer provide s type safety and self -documenting 
+features through JSON schema -based interfaces.  
+5. Security and Authentication Layer:  Responsible for securing 
+agent communication and tool usage, handling authentication, 
+authorization, and data encryption. According to C loud Security 
+Alliance [15], this layer builds a secure agent collaboration 
+environment using OAuth 2.0 -based authorization mechanisms 
+and end -to-end encryption.  
+This layered architecture improves system maintainability and 
+scalability through separation o f concerns. Each layer can be 
+developed and tested independently and can be extended or 
+replaced as needed, allowing the architecture to evolve flexibly with 
+the agent system.  
+ 
+B. A2A Implementation Procedure  
+The implementation of the A2A protocol involves a structured 
+sequence of steps, including the definition of agent metadata, the 
+setup of communication logic, and task management. These steps 
+ensure that agents can interact effectively in a standardized and 
+interoperable manner.  
+1. Agent Card Definition  
+An ag ent card is JSON -formatted metadata that specifies an 
+agent’s functionality and role. Agent card definition consists of the 
+following steps [8]: 
+1. Define Agent Identifier and Basic Information:  Define basic 
+information such as agent ID, name, description,  and version.  
+2. Define Capabilities: Define the list of functions provided by the 
+agent, including parameters and return values for each function.  
+3. Define Authentication Requirements: Define authentication 
+Fig. 2. The MCP×A2A Framework integrates agent -to-agent communication with tool connectivity in a layered architecture  
+ 
+
+--- Page 6 ---
+ 
+methods and permission scopes required for agen t usage.  
+4. Add Metadata: Define additional metadata such as agent 
+category, tags, and creator information.  
+Agent cards, as JSON -formatted metadata, define agent ID, 
+function list, and authentication requirements, enabling other agents 
+to discover and util ize them [8]. According to Salimbeni [16], 
+standardized agent cards support the dynamic scalability of the agent 
+ecosystem, allowing existing agents to automatically recognize and 
+collaborate with new agents added to the system.  
+2. Message Processing Implemen tation  
+A2A message processing is implemented through the following 
+steps [8]: 
+1. Message Reception:  Receive messages from other agents 
+through various communication channels such as HTTP 
+requests, message queues, or WebSocket.  
+2. Message Parsing:  Parse the  received message to extract 
+headers, bodies, and parts. A2A messages are structured in 
+JSON format, making parsing easy.  
+3. Message Validation:  Validate the message format and content, 
+including schema validation, signature verification, and 
+permission ch ecks.  
+4. Message Processing:  Perform appropriate tasks according to 
+the message content, including function execution, state 
+updates, and information provision.  
+5. Response Generation:  Generate a response message according 
+to the task result. The response must comply with the A2A 
+message format and include all necessary information.  
+6. Response Transmission:  Send the generated response to the 
+original message sender through the same communication 
+channel used for the original request.  
+This message processing can be performed asynchronously, and 
+additional messages may be exchanged to update the status of long -
+running tasks. According to Habler et al. [13], the asynchronous 
+nature of A2A message processing enables effective agent 
+coordin ation in complex collaboration scenarios.  
+3. Task Management Implementation  
+A2A task management is implemented with the following 
+elements [8]:  
+1. Task Creation:  Create a new task according to the client’s 
+request. Each task has a unique identifier and includ es 
+information such as task type, parameters, and status.  
+2. Task Status Tracking:  Track and update task progress. Task 
+status can be created, in progress, completed, or failed, and 
+timestamps are recorded for status changes.  
+3. Task Result Management:  Man age the results (artifacts) when 
+a task is completed. Results can be in various forms (documents, 
+code, images, etc.) and are stored with metadata.  
+4. Task Error Handling:  Manage error information and execute 
+appropriate recovery strategies in case of task  failure. Error 
+information may include error codes, messages, and stack 
+traces.  
+Task management implementation enables effective coordination 
+and management of long -running tasks among agents. According to 
+Pajo P. [20], the task management mechanism of A2 A facilitates agent 
+coordination in complex collaboration scenarios and provides a 
+foundation for implementing recovery strategies in case of task 
+failure.  C. MCP Implementation Procedure  
+The implementation of the MCP involves defining tool 
+specifications, ha ndling function invocations, and managing 
+interactions between agents and external tools. The following 
+outlines the major stages of MCP implementation.  
+1. Tool Description Definition  
+In MCP, tool descriptions are defined using JSON schema. Tool 
+description d efinition consists of the following steps [8], [14]: 
+1. Define Tool Basic Information:  Define basic information such as 
+tool name, description, and version.  
+2. Define Function List:  Define the list of functions provided by the 
+tool, including description, parameters, and return values for 
+each function.  
+3. Define Parameter Schema:  Define JSON schema for each 
+function’s parameters, including type, constraints, and required 
+status.  
+4. Define Return Value Schema:  Define JSON schema for each 
+function’s return value, including type, structure, and expected 
+values.  
+These tool descriptions provide a foundation for agents to 
+understand and utilize tool functionality and usage. According to 
+Schmid [21], the self -documen ting nature of MCP enables developers 
+to understand tool usage without separate documentation, 
+significantly improving development productivity.  
+2. Function Call Processing Implementation  
+MCP function call processing is implemented through the following 
+steps  [8, 14]:  
+1. Function Call Request Reception:  Receive function call requests 
+from agents in various forms such as HTTP requests or RPC calls.  
+2. Parameter Validation:  Validate that the requested parameters 
+match the schema defined in the tool description, including 
+type checks, required parameter checks, and value range 
+validation.  
+3. Function Execution:  Execute the actual function using the 
+validated parameters, which may include internal logic 
+execution, external API calls, or database queries.  
+4. Result Conversion:  Convert the function execution result to the 
+return format defined in the tool description, including data 
+format conversion, field mapping, and error handling.  
+5. Response Return:  Return the converted result to the agent in 
+the form of an HTTP  response, RPC response, etc.  
+This function call processing implementation enables agents to 
+effectively utilize external tools and APIs. According to KeywordsAI 
+[22], the type safety and schema -based validation of MCP significantly 
+reduce errors in agent -tool interaction, improving system stability 
+and reliability.  
+ 
+D. Integrated Implementation of A2A and MCP  
+Integrating the A2A and MCP protocols enables the development 
+of highly interoperable and extensible agent systems that combine 
+inter -agent collaboratio n with structured tool interaction. The 
+following subsections present a step -by-step methodology for 
+implementing such an integrated system.  
+1. Integrated Architecture Design  
+The integrated architecture of A2A and MCP can be designed with 
+the following compon ents [7], [14]: 
+
+--- Page 7 ---
+ 
+1. A2A Communication Module:  Responsible for agent -to-agent 
+communication, implementing the A2A protocol to handle 
+message exchange, task management, and agent card 
+processing.  
+2. MCP Tool Module:  Responsible for connecting with external 
+tools, implementing the MCP protocol to manage tool 
+descriptions, function call processing, and result conversion.  
+3. Agent Core:  Implements the core logic of the agent, handling 
+user request processing, task planning, and decision -making.  
+4. Context Manager : Manages the state and context of the agent, 
+including session management, state tracking, and memory 
+management.  
+5. Security Manager:  Responsible for authentication and 
+authorization, including user authentication, agent -to-agent 
+authentication, and tool  access control.  
+This modular design improves system maintainability and 
+scalability through separation of concerns. Each module can be 
+developed and tested independently and can be extended or 
+replaced as needed.  
+2. Utilizing Google ADK (Agent Developer Kit)  
+Google’s ADK (Agent Developer Kit) provides tools and libraries for 
+easily implementing A2A and MCP. Implementation using ADK 
+consists of the following steps [8]: 
+1. ADK Installation:  Install and configure Google ADK, which 
+supports various programming la nguages such as Python, 
+JavaScript, and Java.  
+2. Agent Definition:  Define an agent compliant with the A2A 
+protocol, including writing agent cards, implementing message 
+processing logic, and setting up task management.  
+3. Tool Integration:  Integrate externa l tools using MCP, including 
+writing tool descriptions, implementing functions, and setting 
+up error handling logic.  
+4. Agent Deployment:  Deploy and test the implemented agent. 
+ADK supports various deployment options such as local 
+development environments and cloud environments.  
+ADK abstracts the complexity of A2A and MCP, allowing 
+developers to focus on core agent functionality. According to Pajo P. 
+[20], using ADK can reduce agent development time by up to 60% and 
+significantly improve code quality and st andard compliance.  
+3. Implementation Strategy Using LangGraph  
+LangGraph can be utilized to construct multi -agent workflows, 
+enabling the definition of flows that include cycles —an essential 
+feature for most agent architectures [23], [24]. LangGraph is an agen t 
+framework that supports the A2A protocol and enables integrated 
+implementation of A2A and MCP. As shown in Figure 3, using the 
+LangChain MCP adapter library makes it possible to connect to 
+multiple MCP servers and load tools from those servers [25]. 
+ 
+ 
+Fig. 3. LangChain MCP Adapters  MCP tools can be converted into LangChain tools and used with 
+LangGraph agents, and a client implementation that connects to 
+multiple MCP servers and loads tools from them is feasible. 
+LangGraph can be used to implement agen ts compliant with the A2A 
+protocol and integrate tools such as currency exchange rate lookup 
+tools via MCP. The agent processes user requests and, as needed, 
+calls the currency exchange rate lookup tool via MCP to return results. 
+Through this process, the integrated use of A2A and MCP enables the 
+extension of agent functionality and the standardization of 
+connections with external tools.  
+ 
+E. Implementation Considerations  
+When implementing an integrated agent system using A2A and 
+MCP, several key factors must be considered to ensure security, 
+scalability, reliability, and maintainability. This section outlines the 
+primary technical considerations and recommended best practices.  
+1. Security and Authentication:  Appropriate authentication and 
+authorization mechani sms must be implemented to secure 
+agent communication and tool usage. According to Cloud 
+Security Alliance [15], security implementation for A2A and 
+MCP should utilize standard technologies such as OAuth 2.0, 
+JWT, and TLS.  
+2. Scalability:  The system must b e designed to accommodate 
+increasing loads and new requirements. This can be achieved 
+through horizontal scaling, asynchronous processing, and 
+caching.  
+3. Error Handling:  Mechanisms must be implemented to 
+appropriately handle various error situations such as 
+communication errors and tool failures. This can be achieved 
+through strategies such as retry, fallback paths, and graceful 
+degradation.  
+4. Performance Optimization:  Strategies to minimize latency in 
+agent communication and tool usage should be applied.  This 
+can be achieved through connection pooling, batch processing, 
+and asynchronous I/O.  
+5. Monitoring and Logging:  Logging mechanisms must be 
+implemented to monitor system operation and diagnose 
+problems. This can be achieved through structured logging, 
+distributed tracing, and metric collection.  
+Implementation that appropriately reflects these considerations 
+can significantly improve the stability, security, and scalability of 
+agent systems. According to Habler et al. [13], implementation of A2A 
+and MCP that incorporates these considerations is a key factor in 
+promoting the actual adoption of agent technol ogy in enterprise 
+environments.  
+IV. USE CASES OF A2A  AND MCP  
+A. Application Areas in Enterprise Environments  
+A2A and MCP can be utilized in various ways in ent erprise 
+environments. This section examines various use cases of how these 
+two protocols can be applied in real business settings.  
+1. Recruitment Process Automation  
+As demonstrated in Google’s official demo [7], A2A and MCP can 
+be effectively leveraged to aut omate the recruitment pipeline. The 
+following agents collaborate across various stages of the hiring 
+process:  
+1. Candidate Screening Agent:  Analyzes resumes and evaluates 
+basic qualifications. This agent connects via MCP to resume 
+
+
+--- Page 8 ---
+ 
+parsing tools and qualifi cation databases to perform efficient 
+screening.  
+2. Interview Scheduling Agent:  Coordinates schedules between 
+interviewers and candidates and schedules interviews. 
+According to Salimbeni [16], this agent connects via MCP to 
+calendar APIs and email systems to automate complex 
+scheduling tasks.  
+3. Interview Preparation Agent:  Prepares customized interview 
+questions based on the candidate’s background. This agent 
+receives candidate information from the screening agent via 
+A2A and generates appropriate question s. 
+4. Feedback Collection Agent:  Collects and summarizes feedback 
+from interviewers after interviews. This agent connects via MCP 
+to feedback forms and evaluation systems to collect structured 
+feedback.  
+These agents communicate with each other via the A2A protocol 
+and connect to external tools such as email systems, calendar 
+management tools, and HR management systems via MCP. According 
+to Pajo P. [20], such automation systems can improve recruitment 
+process efficiency by up to 40% and significantly enhance  candidate 
+experience. For example, in a company processing 1,000 candidates, 
+a system based on A2A and MCP reduced average processing time 
+from 15 to 9 days and achieved an ROI of 120% within 3 months. Initial 
+development required about 2 weeks for API in tegration and agent 
+setup, but maintenance costs were kept below $5,000 per month 
+thanks to standardized protocols.  
+2. Customer Support System  
+A2A and MCP also offer substantial benefits in modernizing 
+customer support systems. The following agent roles are typically 
+involved [8]:  
+1. Initial Response Agent:  Receives customer inquiries and 
+collects basic information. This agent connects via MCP to 
+customer databases and previous inquiry history to understand 
+customer context.  
+2. Problem Diagnosis Agent:  Analyz es and diagnoses customer 
+problems. According to Habler et al. [13], this agent receives 
+customer information and problem descriptions from the initial 
+response agent via A2A and performs accurate diagnosis.  
+3. Solution Proposal Agent:  Proposes appropriate  solutions based 
+on diagnosis results. This agent connects via MCP to knowledge 
+bases and product information systems to provide customized 
+solutions.  
+4.Escalation Agent:  Escalates complex problems to human agents. 
+This agent collaborates with other agent s via A2A to determine 
+the need for escalation and connects via MCP to ticketing 
+systems for smooth handover.  
+These agents collaborate via A2A and connect to external tools 
+such as CRM systems, knowledge bases, and ticketing systems via 
+MCP. According to C loud Security Alliance [15], such systems can 
+reduce average inquiry resolution time by 60% and increase first 
+contact resolution rate by more than 25%.  
+3. Code Review and Quality Management  
+A2A and MCP can be applied to automate code review and 
+software qual ity assurance tasks. The following agents participate in 
+this workflow [7]: 
+1. Code Analysis Agent:  Analyzes code structure, complexity, and 
+coding standard compliance. This agent connects via MCP to 
+static analysis tools and code metric systems to perform  in-
+depth code analysis.  2. Security Review Agent:  Checks code for security vulnerabilities. 
+According to KeywordsAI [22], this agent connects via MCP to 
+security scanners and vulnerability databases to identify 
+potential security risks.  
+3. Performance Opt imization Agent:  Identifies performance 
+bottlenecks and suggests optimization strategies. This agent 
+collaborates with the code analysis agent via A2A to identify 
+performance issues and connects via MCP to profiling tools to 
+provide specific optimization s trategies.  
+4. Documentation Support Agent:  Evaluates code documentation 
+quality and suggests improvements. This agent connects via 
+MCP to documentation generation tools and API 
+documentation systems to support effective documentation.  
+These agents collabor ate via A2A and connect to development 
+tools such as version control systems, CI/CD pipelines, and issue 
+trackers via MCP. According to Schmid [21], such systems can improve 
+code quality by an average of 35% and increase developer 
+productivity by more than  20%.  
+4. Developer Support System  
+In developer enablement, A2A and MCP can be used to construct 
+intelligent assistants that automate routine development tasks [8]:  
+1. Code Generation Agent:  Generates code according to 
+developer requirements. This agent connec ts via MCP to code 
+repositories and API documentation to generate code 
+appropriate for the project context.  
+2. Debugging Support Agent:  Analyzes the cause of errors and 
+suggests solutions. According to the Model Context Protocol 
+specification [14], this ag ent connects via MCP to log systems, 
+debuggers, and error databases to support effective debugging.  
+3. Learning Material Provision Agent:  Provides developers with 
+necessary learning materials and documentation. This agent 
+collaborates with other agents via  A2A to understand the 
+developer’s current task context and connects via MCP to 
+documentation systems and learning platforms to provide 
+customized learning materials.  
+4. Task Management Agent:  Tracks developer tasks and manages 
+schedules. This agent connec ts via MCP to project management 
+tools and scheduling systems to support efficient task 
+management.  
+These agents collaborate via A2A and connect to tools such as IDEs, 
+documentation systems, and project management tools via MCP. 
+According to Pajo P. [20], such systems can improve developer 
+productivity by up to 50% and significantly improve code quality and 
+documentation standards.  
+ 
+B. Implementation Case Study: Stock Information System  
+This section introduces a practical application case that applies the 
+impl ementation methodology of A2A and MCP described in Chapter 
+3. This case uses LangChain and LangGraph to build a system that 
+retrieves and analyzes various stock information, with multiple 
+specialized agents collaborating to process users’ complex financial  
+information requests.  In Korea, there has been a recent surge in 
+interest in the U.S. stock market, accompanied by a significant 
+increase in the demand for related research and information [17], 
+[18]. The widespread public interest in stock investment transcends 
+age and gender, yet individuals with limited expertise in the stock 
+market often encounter difficulties in searching for and interpreting 
+relevant information [19]. To effectively address thes e challenges, the 
+Stock Information System has been developed to provide users with 
+
+--- Page 9 ---
+ 
+systematic and useful information through a question -and-answer 
+function based on natural language processing, leveraging the 
+capabilities of various agents.  
+The case syste m is designed so that when a user asks a question 
+about stock or company information in natural language, multiple 
+specialized agents collaborate to provide a comprehensive answer. 
+The system uses A2A protocol -based agent communication and MCP -
+based extern al tool access as core technologies. Main functions 
+include:  
+1. Stock Price Lookup:  Provides basic stock information such as 
+current price, change rate, and trading volume for a specific 
+company.  
+2. Listed Company News Lookup:  Collects the latest news and 
+disclosure information related to the company.  
+3. Listed Company Status Lookup:  Provides basic information, 
+financial statements, and key indicators of the company.  
+4. Company Analysis:  Integrates collected information to prov ide 
+SWOT analysis and investment perspective analysis.  
+1. System Architecture  
+The system strictly follows the layered structure presented in 
+Chapter 3 and consists of the following main layers:  
+1. User Interface  Layer:  User interface and multilingual support.  
+2. Agent Management Layer:  Agent orchestration and workflow 
+management.  
+3. Core Protocol Layer:  Implementation of A2A and MCP 
+protocols.  
+4. Tool Integration Layer:  Integration with external data sources 
+and APIs.  
+5. Security Layer:  Authentication and auth orization management.  
+ 
+The system follows a multi -layered architecture, as shown in Figure 4 . 
+ 
+ 
+Fig. 4. Project Source Structure – Multi -Layer Architecture for Stock 
+Information System  
+ The user interface layer includes a multilingual input module. The 
+agent management layer is a LangGraph -based workflow manager. 
+The core protocol layer includes an A2A message handler and an MCP 
+tool call module. The tool integration layer includes financial APIs and 
+news scrapers. The security layer includes an OAuth 2.0 -based 
+authentication module.  
+2. System Implementation  
+1. A2A Module Implementation  
+The A2A protocol was implemented in the LangChain and 
+LangGraph  environment. Agent communication follows a 
+standardized JSON message format, and each agent has a unique ID 
+and role.  
+a. Agent Card Definition  
+Each agent is defined by a JSON -formatted “agent card,” which 
+specifies the agent’s metadata and functionality. Figure 5 shows an 
+example of a stock price lookup agent card.  
+ 
+ 
+Fig. 5. Example of Stock Price Lookup Agent Card  
+ 
+b. A2A Message Structure and Agent Registry  
+Agent communication follows a standardized message structure, 
+and the system supports agent searc h and function lookup through 
+an agent registry that manages agent cards. Figure 6 shows part of 
+the code for the A2AMessage class, which defines methods for 
+serializing and deserializing message objects in JSON format, used to 
+implement the A2A protocol. This code enables conversion of 
+messages to standardized JSON format for agent communication and 
+restoration of JSON data to message objects.  
+ 
+ 
+Fig. 6. A2A Message Structure and Agent Registry Sample Code  
+ 
+
+
+--- Page 10 ---
+ 
+2. MCP Module Implementation  
+Each agent can call  one or more independent modules according 
+to the MCP method to connect with external data sources and 
+perform preprocessing and analysis. In this system, tools such as stock 
+data, news scraping, company information, financial data, and 
+analysis engines ar e integrated via MCP.  
+a. stock_data – Stock Price Lookup Module:  Returns real -time 
+price data for a specific stock via financial data APIs (e.g., Yahoo 
+Finance, Alpha Vantage).  
+b. web_scraper – Listed Company News Collection Module:  
+Operates as a simple we b crawler to collect news article headlines 
+based on company names. Can also be linked to disclosure 
+information APIs as needed.  
+c. financial_data – Financial Information Lookup Module:  Parses 
+and returns key data from financial statements based on K -IFRS 
+standards. This module is linked to the company status lookup agent.  
+d. analysis_engine – Integrated Analysis Module:  Uses LangChain -
+based LLM to generate text -based insights. Integrates company status 
+and news data to derive future outlook for the company . 
+Each tool is defined by a JSON -formatted description file that 
+specifies the tool’s functionality and parameters. Figure 7 shows 
+sample code for the stock_data module, where the MCP client loads 
+the tool description and processes function calls. The prov ided code 
+includes the extract_tickers function, which extracts stock tickers 
+from user input strings. This function handles tickers in various 
+formats (English tickers, Korean stock codes, company names, etc.) 
+and is used in the stock information system b ased on the A2A and 
+MCP framework described in Section IV .C of the paper, integrating 
+with external financial data sources (e.g., yfinance).  
+ 
+ 
+Fig. 7. Sample Code for stock_data – Stock Price Lookup Module  
+ 
+3. LangGraph -based Workflow Implementation  
+The system uses LangGraph to manage workflows among agents. 
+Workflow state and workflow graph definitions are implemented. The 
+orchestrator agent analyzes user requests and calls appropriate 
+agents in sequence to ge nerate comprehensive responses.  
+The code in Figure 8 is the _create_workflow method that defines 
+the workflow for the stock information system using LangGraph. This 
+method creates a state -based workflow for processing user requests 
+in a multi -agent system based on the A2A protocol and MCP.   
+Fig. 8. Sample Code Defining Workflow for Stock Information System 
+Using LangGraph  
+ 
+4. User Query Processing Flow  
+The following is an actual example of how the system processes 
+user queries:  
+a. User asks: “Please provide the recent stock price, news, and 
+investment perspective analysis for Samsung Electronics.”  
+b. Orchestrator agent analyzes the query and plans to call stock 
+price lookup, news, company information, financial information, and 
+analysis agents in sequence.  
+(1) Requests stock price information  from the stock price lookup agent.  
+(2) Requests recent news from the news agent.  
+(3) Requests company information from the company information agent.  
+(4) Requests financial data from the financial information agent.  
+(5) Requests integrated analysis from t he analysis agent.  
+c. Each agent accesses the necessary tools via MCP to collect data.  
+d. Analysis agent performs investment perspective analysis by 
+integrating collected information.  
+e. Orchestrator generates a final response and delivers it to the 
+user.  
+3. Implementation Results Analysis  
+The implementation results of the LangGraph example show the 
+following characteristics:  
+1. Concise Implementation:  The complexity of A2A and MCP is 
+abstracted by the framework, allowing developers to focus on 
+core agent func tionality. According to Salimbeni [16], 
+implementation using LangGraph can reduce code volume by 
+more than 70% compared to traditional methods.  
+2. Standard Compliance:  Implemented agents comply with A2A 
+protocol standards and can be easily integrated with other A2A -
+compatible agents, greatly improving ecosystem scalability and 
+interoperability.  
+3. Easy Tool Integration:  Connection with external APIs is 
+simplified via MC P, making it easy to integrate various tools and 
+
+
+--- Page 11 ---
+ 
+services. According to the MCP specification [14], MCP’s 
+standardized interface reduces tool integration time by an 
+average of 65%.  
+4. Scalability:  The basic example can be easily extended to more 
+complex fu nctionalities and multi -agent systems. According to 
+Habler et al. [13], LangGraph -based systems maintain stable 
+performance even when scaled to dozens of agents.  
+ 
+C. Implications of Application Areas and Implementation 
+Cases  
+The various application areas and implementation cases of A2A 
+and MCP provide the following implications:  
+1. Importance of Agent Collaboration: Collaboration among 
+multiple specialized agents is essential for solving complex 
+problems, and A2A effectively supports this. According to Pajo 
+P. [20], collaborative agent systems have problem -solving 
+capabilities more than three times higher than single agents on 
+average.  
+2. Need for Tool Integration: Practical agent systems must be 
+connected to external tools, and MCP supports this in a 
+standardi zed way. According to the MCP specification [14], tool 
+integration via MCP is a key factor in enhancing the practical 
+value of agents.  
+3. Value of Standardization: Standardized protocols facilitate 
+integration of diverse agents and tools, greatly improving  
+ecosystem scalability. According to Schmid [21], standardized 
+protocols can reduce agent development costs by up to 60% 
+and shorten time to market by 40%.  
+4. Importance of Practical Implementation: Beyond theoretical 
+concepts, providing practical implemen tation methodologies 
+and tools accelerates the actual adoption of agent technology. 
+According to Habler et al. [13], developer -friendly tools and 
+clear implementation guidelines can increase adoption rates of 
+agent technology by more than three times.  
+Thes e implications demonstrate that A2A and MCP are not just 
+technical protocols but core elements for building practical agent 
+ecosystems. The integrated use of these two protocols is expected to 
+play an important role in advancing agent technology from the 
+experimental stage to actual service application.  
+V. DISCUSSION AND CONCLUSION  
+A. Summary and Conclusion  
+This paper systematically analyzed the technical structure and 
+operational principles of Google’s A2A protocol and Anthropic’s MCP, 
+and explored how these two  protocols complement each other to 
+build a practical agent ecosystem. A2A enables standardized 
+communication and collaboration among heterogeneous agents, 
+while MCP is a standardized protocol designed to allow agents to 
+securely connect with various exter nal tools, APIs, and resources.  
+A2A enhances interoperability and collaboration efficiency among 
+agents through mechanisms such as agent cards, structured 
+messages, and task management. MCP enables agents to 
+interactively integrate with external resources through JSON schema -
+based tool descriptions, function calling, and context management. 
+These two protocols operate independently but, when used together, 
+form the foundation for building practical and scalable multi -agent -
+based AI systems. From an implemen tation perspective, the case 
+study using LangGraph and other frameworks detailed the process of translating theoretical concepts into real -world applications. Practical 
+approaches for securing interoperability and improving development 
+efficiency for LLM -based autonomous agent systems in various 
+enterprise environments were suggested through the 
+complementary use of A2A and MCP. Use cases such as recruitment 
+automation, customer support, code review, and developer support 
+demonstrated the practical applicab ility of the proposed approach. In 
+particular, the stock information system implementation case 
+demonstrated that the integrated use of A2A and MCP is applicable 
+to complex real -world scenarios.  
+This analysis and case study suggest that A2A and MCP are not  just 
+technical protocols but core elements for building practical agent 
+ecosystems. The introduction of standardized protocols can 
+significantly reduce the development and deployment costs of agent 
+systems and greatly improve interoperability and scalabil ity. 
+B. Significance and Limitations of the Study  
+The significance of this study lies in its systematic analysis of the 
+technical structure and operational principles of A2A and MCP, which 
+provided essential knowledge for understanding and practically 
+impleme nting these protocols. Practical implementation case studies 
+and methodologies offered actionable guidelines for developers and 
+enterprises to integrate agent technology into real -world services. 
+Various use cases demonstrated that A2A and MCP can facilita te the 
+transition of agent technology from experimental applications to 
+practical, real -world services.  
+However, the study is not without limitations. Both A2A and MCP 
+are relatively new protocols, having been released in 2024 –2025, and 
+their long -term sta bility in large -scale production environments, such 
+as medical data processing, remains untested. The implementation 
+cases primarily focused on stock information systems, leaving the 
+applicability of these protocols to other industries, such as complex 
+supply chain management or multi -modal processing (e.g., voice and 
+video), largely unexplored. Additionally, the study’s analysis of 
+security and privacy aspects was insufficient, necessitating further 
+research to ensure safety in actual deployment scenarios.  
+C. Future Research Directions  
+Future research should focus on enhancing the practicality and 
+scalability of A2A and MCP. First, scalability issues such as 
+performance bottlenecks, message routing, and state management in 
+systems with hundreds or thousands of  collaborating agents must be 
+addressed. Second, research is needed to strengthen security, 
+including encryption, authentication, access control, and audit trails 
+for agent communication. Third, industry -specific architectures and 
+application cases for fin ance, healthcare, manufacturing, etc., should 
+be explored. Fourth, an open governance model should be 
+established through collaboration with standardization bodies and 
+stakeholders for the long -term development of the protocols. Finally, 
+research on agent systems that process not only text but also images, 
+voice, and video (multi -modal data) is required. These directions will 
+contribute to advancing A2A and MCP as practical solutions in diverse 
+environments.  
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+
+--- Page 12 ---
+ 
+REFERENCES  
+[1] C., Jeong, “Beyond Text: Implementing Multimodal Large Language 
+Model -Powered Multi -Agent Systems Using a No -Code Platform,” 
+Journal of Intelligence and Inform ation Systems , 31(1), 2025, pp. 191 –
+231. doi:10.13088/jiis.2025.31.1.191  
+[2] C., Jeong, “A Study on the Implementation of Generative AI Services 
+Using an Enterprise Data -Based LLM Application Architecture,” Advances 
+in Artificial Intelligence and Machine Learn ing, 3(4), 2023, pp. 1588 –1618. 
+doi: 10.54364/AAIML.2023.1191  
+[3] C., Jeong, “Generative AI service implementation using LLM application 
+architecture: based on RAG model and LangChain framework,” Journal 
+of Intelligence and Information Systems , 19(4), 2023, pp. 129 –164. doi: 
+10.13088/jiis.2023.29.4.129  
+[4] S., Yang, O., Nachum, Y., Du, J., Wei, P., Abbeel, D., Schuurmans, 
+“Foundation models for decision making: Problems, methods, and 
+opportunities,” arXiv preprint  arXiv:2303.04129, 2023.  
+[5] E., Karpas , et al., “AgentBench: Evaluating LLMs as Agents,” arXiv 
+preprint arXiv:2308.03688, 2023.  
+[6] Anthropic, “Introducing the Model Context Protocol,” 2025. Accessed: 
+May 31, 2025.  [Online]. Available: 
+https://www.anthropic.com/news/model -context -protocol  
+[7] Google, “Announcing the Agent2Agent Protocol (A2A),” Google 
+Developers Blog, 2025. Accessed: May 31, 2025. [Online]. Available: 
+https://developers.googleblog.com/en/a2a -a-new -era-of-agent -
+interoperability/  
+[8] Google, “Agent2Agent (A2A) Protocol Specification,” 2025. Accessed: 
+May 31, 2025. [Online]. Available: 
+https://google.github.io/A2A/specification/  
+[9] C., Jeong, S., Sim, H., Cho, S., Kim, B., Shin, “E2E Process Automation 
+Leveraging Generative AI and IDP -Based Automation Agent: A Case Study 
+on Corporate Expense Proc essing,” arXiv preprint  arXiv:2505.20733, 
+2025.  
+[10] R., Margaret, M., Natalie, “Techopedia, Autonomous Agent,” 2025. 
+Accessed: May 31, 2025. [Online]. Available:  
+https://www.techopedia.com/definition/autonomous -agent, last  
+[11] Salesforce, “What are Autonomous Ag ents? A Complete Guide,” 2025. 
+Accessed: May 31, 2025. [Online]. Available:  
+https://www.salesforce.com/eu/agentforce/ai -agents/autonomous -
+agents/  
+[12] T., Finin, R., Fritzson, D., McKay, R., McEntire, “KQML as an agent 
+communication language,” in Proceedings o f the third international 
+conference on Information and knowledge management , 1994, pp. 456 –
+463.  
+[13] I., Habler, K., Huang, V. S., Narajala, P., Kulkarni, “Building a secure 
+agentic AI application leveraging A2A protocol,” arXiv preprint  
+arXiv:2504.16902, 2025 . 
+[14] Model Context Protocol, “Model Context Protocol Specification,” 2025. 
+Accessed: May 31, 2025. [Online]. Available:  
+https://modelcontextprotocol.io/specification/2025 -03-26 
+[15] Cloud Security Alliance, “Threat Modeling Google's A2A Protocol with 
+the MAESTRO Framework,” 2025. Accessed: May 31, 2025. [Online]. 
+Available:  https://cloudsecurityalliance.org/blog/2025/04/30/threat -
+modeling -google -s-a2a-protocol -with -the-maestro -framework  
+[16] G., Salimbeni, “Understanding Google’s A2A Protocol: Why We Need a 
+Standard f or AI Agent Communication,” 2025. Accessed: May 31, 2025.  
+[Online]. Available:  https://guido -
+salimbeni.medium.com/understanding -googles -a2a-protocol -why-we-
+need -a-standard -for-ai-agent-communication -ac9188adb0cc  
+[17] H., Jo, J., Choi, J., Seo, “OAR algorithm te chnology based on opinion 
+mining utilizing stock news contents,” Journal of KIIT , 13(3), 2015, pp. 
+111–119. doi: 10.14801/jkiit.2015.13.3.111  
+[18] J. H., Moon, J. J., Sohn, S. J., Yu, “Stock portfolio analysis and visualization 
+services using gamification,” Journal of KIIT , 20(1), 2022, pp. 191 –198. 
+doi: 10.14801/jkiit.2022.20.1.191  
+[19] N., Kim, S., Yu, “Stock information retrieval and analysis techniques using 
+Korean text queries,” Journal of the Korea Institute of Information 
+Technology , 20(9), 2022, pp. 13 –18. 
+[20] P., Pajo, “Comprehensive Analysis of Google's Agent2Agent (A2A) Protocol: Technical Architecture, Enterprise Use Cases, and Long -Term 
+Implications for AI Collaboration,” 2025. Accessed: May 31, 2025. 
+[Online]. Available:  
+https://www.researchgate.net/publ ication/390694531_Comprehensive
+_Analysis_of_Google's_Agent2Agent_A2A_Protocol_Technical_Architec
+ture_Enterprise_Use_Cases_and_Long -
+Term_Imp lications_for_AI_Collaboration  
+[21] P., Schmid, “Model Context Protocol (MCP) an overview,” 2025  Accessed: 
+May 31, 2025. [ Online]. Available:  https://www.philschmid.de/mcp -
+introduction1  
+[22] KeywordsAI, “A Complete Guide to the Model Context Protocol (MCP) in 
+2025,” 2025. Accessed: May 31, 2025. [Online]. Available:  
+https://www.keywords ai.co/blog/introduction -to-mcp  
+[23] C., Jeong, “A Graph -Agent -Based Approach to Enhancing Knowledge -
+Based QA with Advanced RAG,” Knowledge Management Research , 
+25(3), 2024, pp. 99 –119. doi: 10.15813/kmr.2024.25.3.005  
+[24] LangGraph, 2025. Accessed: May 31, 2025. [Online]. Available:  
+https://lan gchain -ai.github .io/langgraph/  
+[25] Langchain, “LangChain MCP Adapters,” 2025. Accessed: May 31, 2025. 
+[Online]. Available:  https://github.com/langc hain -ai/langchain -mcp -
+adapters

data/documents/metadata/0e22c68a-134e-4474-af0d-1c119b576bec.json

@@ -0,0 +1,17 @@
+{
+  "id": "0e22c68a-134e-4474-af0d-1c119b576bec",
+  "filename": "tools_discovery.png",
+  "doc_type": "image",
+  "file_size": 78410,
+  "created_at": "2025-07-08T12:16:07.737267",
+  "metadata": {
+    "file_extension": ".png",
+    "content_length": 350,
+    "word_count": 43
+  },
+  "tags": [],
+  "summary": null,
+  "category": null,
+  "language": null,
+  "content_length": 350
+}

data/documents/metadata/b229c249-b844-4579-beb1-2d4185eeb732.json

@@ -0,0 +1,17 @@
+{
+  "id": "b229c249-b844-4579-beb1-2d4185eeb732",
+  "filename": "Model Context Protocol MCPLandscapeSecurityThreats.pdf",
+  "doc_type": "pdf",
+  "file_size": 1108266,
+  "created_at": "2025-07-08T12:16:21.530542",
+  "metadata": {
+    "file_extension": ".pdf",
+    "content_length": 80288,
+    "word_count": 10899
+  },
+  "tags": [],
+  "summary": null,
+  "category": null,
+  "language": null,
+  "content_length": 80288
+}

data/documents/metadata/b3d0767c-1dd8-4f05-a9f8-d69d82f2abde.json

@@ -0,0 +1,17 @@
+{
+  "id": "b3d0767c-1dd8-4f05-a9f8-d69d82f2abde",
+  "filename": "A Study on the MCP  A2A Framework.pdf",
+  "doc_type": "pdf",
+  "file_size": 1109775,
+  "created_at": "2025-07-08T12:58:00.140462",
+  "metadata": {
+    "file_extension": ".pdf",
+    "content_length": 60652,
+    "word_count": 8322
+  },
+  "tags": [],
+  "summary": null,
+  "category": null,
+  "language": null,
+  "content_length": 60652
+}

data/documents/metadata/e3591269-f449-4f88-8185-7d98ef4c4845.json

@@ -0,0 +1,17 @@
+{
+  "id": "e3591269-f449-4f88-8185-7d98ef4c4845",
+  "filename": "A Comprehensive Overview of Large Language Models.pdf",
+  "doc_type": "pdf",
+  "file_size": 1814502,
+  "created_at": "2025-07-08T12:30:52.899470",
+  "metadata": {
+    "file_extension": ".pdf",
+    "content_length": 277778,
+    "word_count": 42622
+  },
+  "tags": [],
+  "summary": null,
+  "category": null,
+  "language": null,
+  "content_length": 277778
+}

data/vector_store/content_index.index

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a4c8c6524348ec9e7e2e23bf1061354fd46ad4f61558c404b1766864fb69fe2f
+size 1439277