MCP Documentation Server

A Model Context Protocol (MCP) server that provides AI assistants with the ability to search and retrieve documentation from popular Python libraries including LangChain, LlamaIndex, and OpenAI.

Features

• Documentation Search: Search through official documentation for supported libraries
• Web Scraping: Fetches and parses documentation content from official sources
• MCP Integration: Works seamlessly with MCP-compatible AI assistants
• Error Handling: Robust error handling with comprehensive logging
• Environment Configuration: Easy setup with environment variables

Supported Libraries

• LangChain - python.langchain.com/docs
• LlamaIndex - python.llama_index.com/docs
• OpenAI - platform.openai.com/docs

Project Structure

mcp/
├── src/
│   ├── claude_invoke/           # Claude AI integration utilities
│   │   └── invoke.py
│   ├── config/                  # Configuration files
│   ├── mcp_server/             # Main MCP server implementation
│   │   ├── main.py             # Main server entry point
│   │   ├── pyproject.toml      # Project dependencies
│   │   ├── uv.lock            # Lock file for dependencies
│   │   └── logs/              # Server logs
│   └── utils/
│       └── logger/            # Logging utilities
│           ├── logging_manager.py
│           └── logging.py
├── .env                       # Environment variables
├── .gitignore
└── README.md

Prerequisites

• Python 3.12+
• uv (Python package manager)
• Serper API Key (for web search functionality)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd mcp

2. Install dependencies:

   cd src/mcp_server
   uv sync

3. Set up environment variables: Create a .env file in the project root:

   SERPER_API_KEY=your_serper_api_key_here

Configuration

For VS Code with Claude Desktop

1. Configure the MCP server using the Command Palette:

   • Open Command Palette (Ctrl+Shift+P)
   • Run MCP: Add Server
   • Enter the following configuration:
     • Server Name: documentation
     • Command: uv
     • Arguments:
       • --directory
       • E:/CodeBase/GitHub/mcp
       • run
       • src/mcp_server/main.py

2. Enable MCP and Agent mode in VS Code settings:

   {
     "chat.mcp.enabled": true,
     "chat.agent.enabled": true
   }

For Claude Desktop

Add to your Claude configuration file:

{
  "mcpServer": {
    "documentation": {
      "command": "uv",
      "args": [
        "--directory",
        "E:/CodeBase/GitHub/mcp",
        "run",
        "src/mcp_server/main.py"
      ]
    }
  }
}

Usage

Once configured, the MCP server provides the following tool:

get_docs(query: str, library: str)

Search documentation for a specific library.

Parameters:

• query: Search query (e.g., "vector store", "embeddings")
• library: Library to search ("langchain", "llama_index", or "openai")

Example Usage:

Can you search for information about Chroma vector database in LangChain?

The assistant will use the get_docs tool to search LangChain documentation and return relevant information.

Development

Running the Server Standalone

For testing and development:

cd E:/CodeBase/GitHub/mcp
uv run src/mcp_server/main.py

Logging

The server includes comprehensive logging:

• Console output: Formatted logs to stderr for debugging
• File logging: Detailed logs saved to src/mcp_server/logs/general/
• Structured logging: JSON-formatted data logging for analysis

Dependencies

Key dependencies (see src/mcp_server/pyproject.toml):

• fastmcp: MCP server framework
• httpx: HTTP client for web requests
• beautifulsoup4: HTML parsing
• python-dotenv: Environment variable management
• loguru: Advanced logging

API Integration

The server uses the Serper API for web search functionality. You'll need to:

1. Sign up for a Serper API account
2. Get your API key
3. Add it to your .env file

Troubleshooting

Common Issues

1. Module not found errors:

   • Ensure you're running from the project root directory
   • Check that all dependencies are installed with uv sync

2. MCP server not appearing:

   • Verify MCP is enabled in your settings
   • Check server status with MCP: List Servers
   • Try MCP: Reset Cached Tools

3. API errors:

   • Edit .env and add your actual API key
   • Get your API key from https://serper.dev/
   • Verify your SERPER_API_KEY is correctly set
   • Check network connectivity
   • Review logs in the logs/general/ directory

Debug Mode

The server includes extensive debug logging. Check the log files in src/mcp_server/logs/general/ for detailed error information.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Support

For issues and questions:

• Check the logs in src/mcp_server/logs/general/
• Review the troubleshooting section above
• Open an issue in the repository

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Note: This MCP server is designed to work with AI assistants that support the Model Context Protocol. Make sure your AI assistant has MCP support enabled and properly configured.