mcp-ragdoc-fork
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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.
The RAG Documentation MCP Server is a robust implementation designed to enhance AI capabilities by integrating documentation retrieval and processing through vector search. This server supports multiple documentation sources and offers semantic search capabilities, allowing AI assistants to provide more contextually relevant responses. It automates documentation processing and provides real-time context augmentation for language models, making it an invaluable tool for developers and AI systems that require access to up-to-date and relevant documentation. The server is equipped with tools for searching, listing, extracting, and managing documentation sources, ensuring comprehensive and efficient documentation handling.
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 stored documents through natural language query and return related fragments
list_sources
List all stored documents in the system
extract_urls
Extract and analyze all URLs from a given webpage
remove_documentation
Delete specific document sources from the system via URL
list_queue
List all URLs in the current document processing queue
run_queue
Process and index all URLs in queues
clear_queue
Delete all pending URLs from the document processing queue
