Qdrant Docs Rag

This MCP server, developed by Hannes Rudolph, enables AI assistants to augment their responses with relevant documentation context through vector-based search and retrieval. Built as a fork of qpd-v's original implementation, it integrates with OpenAI for embeddings generation and Qdrant for vector storage. The server provides tools for adding documentation from URLs, performing semantic searches, extracting links, and managing a processing queue. By connecting AI capabilities with efficient vector search of documentation, this implementation allows AI systems to enhance their knowledge with domain-specific information in real-time. It is particularly useful for building documentation-aware AI assistants, implementing semantic documentation search, and creating context-aware developer tools that require access to up-to-date technical information.

Source

Repository: https://github.com/hannesrudolph/mcp-ragdocs