mcp-ragdocs
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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 powerful tool designed to enhance AI capabilities by integrating relevant documentation context into responses. It leverages vector-based search and retrieval to process multiple documentation sources, enabling semantic search capabilities. This server automates documentation processing and provides real-time context augmentation for language models, making it an essential tool for developers and AI assistants. By indexing and searching through documentation, it allows AI systems to provide more informed and contextually relevant answers. The server supports a variety of operations, including searching documentation, listing sources, extracting URLs, and managing documentation queues, making it versatile for different use cases.
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 for documents through natural language query and return related fragments
list_sources
List all indexed document sources
extract_urls
Extract URLs from web pages and analyze
remove_documentation
Delete the specified document source from the system
list_queue
List all URLs in the document processing queue
run_queue
Process and index all URLs in the queue
clear_queue
Clear all URLs in the document processing queue
