RAG Documentation MCP Server

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Features

• Vector-based documentation search and retrieval
• Support for multiple documentation sources
• Semantic search capabilities
• Automated documentation processing
• Real-time context augmentation for LLMs

Tools

add_documentation

Add documentation from a URL to the RAG database.

Inputs:

• url (string): URL of the documentation to fetch

search_documentation

Search through stored documentation.

Inputs:

• query (string): Search query
• limit (number, optional): Maximum number of results to return (default: 5)

list_sources

List all documentation sources currently stored.

extract_urls

Extract all URLs from a given web page.

Inputs:

• url (string): URL of the page to extract URLs from
• add_to_queue (boolean, optional): If true, automatically add extracted URLs to the queue

queue_documentation

Add URLs to the documentation processing queue.

Inputs:

• urls (string[]): Array of URLs to add to the queue

list_queue

List all URLs currently in the documentation processing queue.

run_queue

Process URLs from the queue one at a time until complete.

remove_documentation

Remove documentation sources by URLs.

Inputs:

• urls (string[]): Array of URLs to remove from the database

Usage

The RAG Documentation tool is designed for:

• Enhancing AI responses with relevant documentation
• Building documentation-aware AI assistants
• Creating context-aware tooling for developers
• Implementing semantic documentation search
• Augmenting existing knowledge bases

Configuration

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "rag-docs": {
      "command": "npx",
      "args": [
        "-y",
        "@hannesrudolph/mcp-ragdocs"
      ],
      "env": {
        "OPENAI_API_KEY": "",
        "QDRANT_URL": "",
        "QDRANT_API_KEY": ""
      }
    }
  }
}

You'll need to provide values for the following environment variables:

• OPENAI_API_KEY: Your OpenAI API key for embeddings generation
• QDRANT_URL: URL of your Qdrant vector database instance
• QDRANT_API_KEY: API key for authenticating with Qdrant

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

Acknowledgments

This project is a fork of qpd-v/mcp-ragdocs, originally developed by qpd-v. The original project provided the foundation for this implementation.