Qdrant

This MCP server, developed by Qdrant, provides a semantic memory layer on top of the Qdrant vector database. Built for use with Claude Desktop, it offers tools for storing and retrieving memories using Qdrant's vector search capabilities. The server leverages FastEmbed models for encoding memories, with the default being 'sentence-transformers/all-MiniLM-L6-v2'. Key features include automatic collection creation, configurable embedding models, and support for both command-line and environment variable configuration. By integrating AI capabilities with Qdrant's efficient vector search, this implementation enhances semantic memory retrieval for AI assistants. It is particularly useful for scenarios requiring persistent memory storage, semantic similarity search, and scalable vector operations in AI applications.

Source

Repository: https://github.com/qdrant/mcp-server-qdrant