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
Related
3 Indexed items
Milvus MCP Server
The zilliztech/mcp-server-milvus project (documented at milvus.io/docs/milvus_and_mcp.md) exposes Milvus vector-database operations to MCP clients such as Claude Desktop and Cursor. The recommended launch path is `uv run src/mcp_server_milvus/server.py --milvus-uri http://localhost:19530` without a separate install step, with optional `MILVUS_URI`, `MILVUS_TOKEN`, and `MILVUS_DB` environment variables. Tools listed in Milvus docs include `milvus-text-search`, `milvus-hybrid-search`, `milvus-multi-vector-search`, `milvus-query`, and `milvus-count` for collection management, semantic retrieval, filtered hybrid search, and entity counts.
ClickHouse MCP Server
The open-source ClickHouse MCP server (PyPI package `mcp-clickhouse`, repository ClickHouse/mcp-clickhouse) exposes MCP tools such as `run_query`, `list_databases`, and paginated `list_tables` against ClickHouse clusters, defaulting to read-only SQL unless `CLICKHOUSE_ALLOW_WRITE_ACCESS` is enabled. Optional chDB extras add `run_chdb_select_query` for embedded queries over files and URLs. HTTP/SSE transports require authentication via `CLICKHOUSE_MCP_AUTH_TOKEN`, FastMCP OAuth/OIDC providers, or explicit `CLICKHOUSE_MCP_AUTH_DISABLED=true` for local dev; a `/health` endpoint supports orchestrator probes without credentials per README guidance.
Neo4j MCP Server
Neo4j documents an official Model Context Protocol server at neo4j.com/docs/mcp/current and in the neo4j/mcp repository. It gives MCP clients structured access to a Neo4j graph: get-schema introspects labels, relationship types, and property keys; read-cypher runs read-only Cypher checked with EXPLAIN and Neo4j query-type classification; write-cypher runs writes unless NEO4J_MCP_READ_ONLY=true; list-gds-procedures lists Graph Data Science procedures when GDS is installed. Prerequisites are a running Aura, Neo4j Desktop, or self-managed database plus the APOC plugin. Local clients use stdio via uvx neo4j-mcp-server or the Homebrew/binary neo4j-mcp, with NEO4J_MCP_URI, NEO4J_MCP_USERNAME, NEO4J_MCP_PASSWORD, and NEO4J_MCP_DATABASE. Aura uses HTTP at https://<instance-id>.mcp-instances.neo4j.io after Tool authentication is enabled in the Aura console. Docs also describe an HTTP transport mode that takes URI and credentials per request and selects the database from the /db/{databaseName}/mcp path.