mcp-ragdocs
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The RAG Documentation MCP Server is a tool designed to enhance AI assistants with relevant documentation context through vector-based semantic search. It supports multiple documentation sources and provides features for automated processing and real-time augmentation of AI responses.
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
- search_documentation: Search through stored documentation using natural language queries.
- list_sources: List all documentation sources currently stored.
- extract_urls: Extract and analyze all URLs from a given web page.
- remove_documentation: Remove specific documentation sources by their URLs.
- list_queue: List all URLs currently waiting in the processing queue.
- run_queue: Process and index all URLs in the queue.
- clear_queue: Remove all pending URLs from the processing queue.
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: You'll need to provide values for the specified environment variables like OPENAI_API_KEY and QDRANT_URL.