Qdrant

Qdrant MCP Server provides semantic search capabilities using a local Qdrant vector database and OpenAI embeddings, built by mhalder with comprehensive TypeScript implementation and 114 unit tests. The server automatically converts text documents to embeddings using OpenAI's models, stores them in a locally-running Qdrant instance via Docker for complete data privacy, and enables natural language search with metadata filtering using Qdrant's powerful filter syntax. It supports full collection lifecycle management (create, delete, info), document operations with automatic UUID normalization for string IDs, and both simple key-value and complex boolean filter expressions, making it valuable for building private knowledge bases, document search systems, and AI assistants that need semantic search without sending data to external vector database services.