| ---
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| title: SEC10QInsight
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| emoji: ⚡
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| colorFrom: purple
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| colorTo: indigo
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| sdk: gradio
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| sdk_version: 5.33.0
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| app_file: app.py
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| pinned: false
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| license: mit
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| short_description: SEC 10-Q data explorer with AI insights
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| tags:
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| - mcp-server-track
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| - agent-demo-track
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| - gradio-mcp-server
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| - sec-10q
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| - financial-insight
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|
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| ---
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| # `📘 SEC10QInsight – A Gradio-Based MCP Server for SEC Financial Analysis`
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| # 🔍 Overview – SEC10QInsight: Agentic SEC Data Analyst
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| 👉 [**Jump to: Try It Yourself ⬇️**](#-try-it-yourself)
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| ---
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| ## 🎥 Demo Video
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| ▶️ **Watch the full demo on YouTube:**
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| [https://youtu.be/vin-Ovz2sFI?si=9n1kIcgnNs-b2K8d](https://youtu.be/vin-Ovz2sFI?si=9n1kIcgnNs-b2K8d)
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| [](https://youtu.be/vin-Ovz2sFI?si=9n1kIcgnNs-b2K8d)
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| ---
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| **SEC10QInsight** is an interactive MCP (Model Context Protocol) server built with **Gradio** that empowers users to query, visualize, and interpret financial data extracted from **SEC 10-Q filings**. Leveraging the power of large language models (LLMs), **SEC10QInsight** translates raw financial disclosures into insightful summaries, trend analysis, and visual dashboards.
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| Built with **Gradio** and powered by the **Model Context Protocol (MCP)**, this app simulates a multi-step agent process:
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| 1. **Retrieve** real-time financial data from the SEC EDGAR database using a CIK (Central Index Key)
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| 2. **Structure** and filter relevant metrics (e.g., `ComprehensiveIncomeNetOfTax`)
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| 3. **Visualize** historical trends using line plots
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| 4. **Analyze** the data using a large language model (LLM) to generate concise insights
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| ---
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| ## 🎯 Purpose
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| This app demonstrates how **agentic workflows** can be applied to **financial intelligence**, allowing a single natural language query to trigger a multi-step, automated reasoning process that includes:
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| - Data retrieval (from SEC)
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| - Contextual structuring and preprocessing
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| - Visualization
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| - LLM-driven interpretation
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| ---
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| ## 🔑 Key Features
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| - 🔍 **Natural language querying** of SEC data (e.g., "Show trends")
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| - 📊 **Interactive data visualization** of quarterly metrics like Comprehensive Income
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| - 🤖 **LLM-powered financial insights**, integrated with models like SambaNova
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| - 🌐 **Live data fetching** from the SEC's EDGAR API (XBRL format)
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| - ⚙️ **Gradio UI** for clean, user-friendly access to analysis tools
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| ---
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| ## 🛠️ Usage
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| 1. **Select a company** (Apple, Tesla, Microsoft) (The sample here for HF MCP Hachathon specifically)
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| 2. **Enter a query**, such as:
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| - *"Summarize the trend"*
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| - *"How did income change over time?"*
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| 3. **Submit the query**
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| 4. The app will:
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| - Fetch SEC 10-Q data
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| - Display it in a table
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| - Render a trend plot
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| - Return a model-generated interpretation
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| ---
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| ## 🤖 Agentic Highlights
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| - **Autonomous orchestration** of data + language processing
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| - **Modular architecture** with MCP for easy extensibility
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| - **Real-world grounding** using factual data from SEC filings
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| ---
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| **SEC10QInsight** is ideal as a reference project or demo for:
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| - Agentic LLM workflows
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| - Financial data applications
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| - LLM × structured data integrations
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| Ideal for researchers, analysts, developers, and AI enthusiasts, **SEC10QInsight** demonstrates how open financial data and cutting-edge AI can work together to deliver explainable, real-time insights.
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| ---
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| ## 🚀 Try It Yourself
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| Users can try this amazing MCP server by accessing the client and code here:
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| 👉 [SEC10QInsight-MCP-CLIENT on Hugging Face Spaces](https://huggingface.co/spaces/Agents-MCP-Hackathon/SEC10QInsight-MCP-CLIENT)
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| ---
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| ## 📦 Tech Stack
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| - Python
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| - Gradio
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| - Pandas & Matplotlib
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| - SEC EDGAR API (XBRL JSON)
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| - OpenAI-compatible LLM client (e.g., SambaNova)
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| - MCP (Model Context Protocol)
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| ---
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| ## 📜 License
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| MIT License — open for learning, building, and extending.
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| Perfect for exploring how LLMs can assist in structured financial analysis through agentic design! |
