RAG Documentation Search

The MCP-server-ragdocs provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. Built with TypeScript, it supports both local (Ollama) and cloud-based (OpenAI) embeddings generation, integrates with Qdrant for vector storage, and includes tools for semantic document search, URL extraction, and queue management. This implementation is particularly valuable for workflows requiring context-aware AI responses backed by specific documentation sources.