Official Qdrant MCP server implementation that gives AI agents a semantic memory layer backed by Qdrant vector search. It exposes MCP tools for storing information and retrieving relevant context, so assistants can persist and recall facts across sessions instead of relying only on short chat history.
Use cases
- Persisting long-lived user preferences for an AI assistant
- Adding semantic recall to multi-step agent workflows
- Building retrieval memory for customer support copilots
- Reusing project context across separate coding sessions
- Grounding responses with previously stored internal notes
Key features
- Claude Desktop
- Cursor
- Codex
Frequently Asked Questions
- Is this an official Qdrant MCP server?
- Yes. Qdrant publishes an official repository and documentation for mcp-server-qdrant.
- What are the core MCP operations?
- The server provides store and find style operations so agents can write memory and retrieve semantically relevant entries.
- Do I need a running Qdrant instance?
- Yes. You connect the MCP server to either Qdrant Cloud or a self-hosted/local Qdrant deployment.
Related
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3 Indexed items
Postgres MCP
pg-mcp-server is a Model Context Protocol server implementation that connects AI agents to PostgreSQL databases. It registers database schemas as MCP resource templates and exposes SQL execution as an MCP tool. Agents can introspect table structures, run parameterized queries, and manage transactions without leaving the chat interface. Designed as a reference implementation for database MCP integrations.
piLoci MCP
piLoci MCP is a self-hosted memory server for AI agents that exposes project-scoped memory storage and retrieval through the Model Context Protocol. Built to run on Raspberry Pi 5, it provides semantic recall, project listing, and user identity tools. Teams connect Claude Desktop, Codex, and other MCP clients to share persistent context without sending memory data to cloud services.
Pinecone MCP
Enables AI agents to inspect Pinecone vector database indexes, query vectors, manage collections, and debug retrieval behavior. Useful for teams using vector search in RAG applications who want to understand index statistics and optimize similarity search performance.