README.md

metadata

title: Doc Mcp
emoji: 🐠
colorFrom: yellow
colorTo: pink
sdk: gradio
sdk_version: 5.33.0
python_version: 3.13
app_file: app.py
pinned: false
license: mit
short_description: 'RAG on documentations for your agent '

Doc-MCP 📚

Transform GitHub documentation repositories into accessible MCP (Model Context Protocol) servers for AI agents

Hackathon Track: mcp-server-track

🎯 What is Doc-MCP?

Doc-MCP ingests markdown documentation from GitHub repositories and creates MCP servers that provide easy access to documentation context for AI agents. Just point it at any GitHub repo with markdown docs, and get an intelligent Q&A interface powered by vector search.

✨ Key Features

• GitHub Integration: Fetch markdown files directly from any GitHub repository
• Vector Search: Uses MongoDB Atlas with Nebius AI embeddings for semantic search
• MCP Server: Exposes documentation as MCP endpoints for AI agents
• Smart Q&A: Ask questions about documentation with source citations
• Repository Management: Track multiple repositories and their statistics

🚀 Quick Start

1. Setup Environment:

# Clone and install
git clone https://github.com/yourusername/doc-mcp.git
cd doc-mcp
uv sync

# Configure environment
cp .env.example .env
# Add your NEBIUS_API_KEY and MONGODB_URI

2. Run the App:

python main.py
# Open http://localhost:7860

3. Ingest Documentation:

   • Enter a GitHub repo URL (e.g., gradio-app/gradio)
   • Select markdown files to process
   • Load files and generate vector embeddings

4. Query Documentation:

   • Select your repository
   • Ask questions about the documentation
   • Get answers with source citations

Workflow

flowchart TD
 subgraph Ingestion["Ingestion"]
        B["Discover Markdown Files"]
        A["GitHub Repo URL"]
        C["User File Selection"]
        D["Chunk & Embed Documents"]
        E["Store in MongoDB"]
  end
 subgraph Query["Query"]
        G["Select Repository"]
        F["User Question"]
        H["Vector Search"]
        I["Retrieve Context"]
        J["Generate Response"]
        K["Display with Sources"]
  end
    A --> B
    B --> C
    C --> D
    D --> E
    F --> G
    G --> H
    H --> I
    I --> J
    J --> K
    E --> H

🛠️ Technology Stack

• Frontend: Gradio
• Vector Store: MongoDB Atlas with vector search
• Embeddings: Nebius AI (BAAI/bge-en-icl)
• LLM: Nebius LLM (Llama-3.3-70B-Instruct)
• Document Processing: LlamaIndex

📹 Demo Video

[Link to demo video showing MCP server in action with Claude Desktop/Cursor]

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Transform your documentation into intelligent, accessible knowledge for AI agents! 🚀