deleted privous vector stores

app.py

@@ -535,4 +535,4 @@ def create_gradio_interface():
 
 if __name__ == "__main__":
     gradio_interface = create_gradio_interface()
-    gradio_interface.launch(mcp_server=True)
+    gradio_interface.launch(server_name="0.0.0.0", server_port=8080, mcp_server=True, ssr_mode=False)

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

@@ -1,21 +0,0 @@
-# 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

@@ -1,977 +0,0 @@
---- 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
-
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-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.
-
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-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.
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-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.
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-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.
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-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.
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-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.
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-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.
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-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
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-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
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-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
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-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
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-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.
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-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
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-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
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-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.
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-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.
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-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.
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-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  
- 
-
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- 
-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  
- 
-
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- 
-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]: 
-
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- 
-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 
-
-
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- 
-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 
-
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- 
-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  
- 
-
-
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- 
-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 ---
- 
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-adapters

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