MCP ConfigsApr 6, 2026·2 min read

Qdrant MCP — Vector Search Engine for AI Agents

MCP server for Qdrant vector database. Gives AI agents the power to store and search embeddings for RAG, semantic search, and recommendation systems. 22,000+ stars on Qdrant.

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Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

Add to your .mcp.json:

{
  "mcpServers": {
    "qdrant": {
      "command": "npx",
      "args": ["-y", "@qdrant/mcp-server"],
      "env": {
        "QDRANT_URL": "http://localhost:6333"
      }
    }
  }
}

Start Qdrant locally: docker run -p 6333:6333 qdrant/qdrant

Restart Claude Code. Ask: "Store this document in the knowledge base" or "Search for similar code patterns."

Intro

Qdrant MCP is a Model Context Protocol server for the Qdrant vector database, giving AI agents like Claude Code the ability to store, search, and manage vector embeddings for RAG pipelines, semantic search, and knowledge management. With 22,000+ GitHub stars on the main Qdrant project, it provides high-performance similarity search with filtering, payload storage, and real-time indexing. Best for developers building AI applications that need persistent semantic memory or retrieval-augmented generation. Works with: Claude Code, Cursor, any MCP client. Setup time: under 3 minutes.

Available Tools

qdrant_store

Store documents with automatic embedding:

"Store this API documentation in the knowledge base under collection 'docs'"

qdrant_search

Semantic search across stored vectors:

"Find documents similar to 'authentication flow with JWT tokens'"

qdrant_delete

Remove specific entries:

"Delete all outdated documentation from the 'docs' collection"

qdrant_list_collections

View all collections and their stats:

"Show all vector collections and their sizes"

Use Cases

RAG Pipeline

Store project documentation, then retrieve relevant context for every query:

1. Index your docs: "Store all markdown files from ./docs into collection 'project-docs'"
2. Query with context: Agent auto-retrieves relevant docs before answering

Code Pattern Search

Store code patterns and search semantically:

"Find code similar to error handling patterns in the auth module"

Knowledge Management

Build a persistent knowledge base your agent can query:

"Remember that we chose PostgreSQL for JSONB support"
→ Stored as vector, retrievable by semantic search

Key Stats

  • 22,000+ GitHub stars (Qdrant)
  • Sub-millisecond search on millions of vectors
  • Filtering + payload storage
  • Real-time indexing
  • Rust-powered performance

FAQ

Q: What is Qdrant MCP? A: Qdrant MCP is a Model Context Protocol server that connects AI agents to the Qdrant vector database for storing and searching embeddings, enabling RAG pipelines and semantic search.

Q: Is Qdrant free? A: Yes, Qdrant is open-source under Apache 2.0. Free self-hosted, with a managed cloud option available.

Q: Do I need to manage embeddings manually? A: The MCP server can auto-generate embeddings using configured models, or you can provide pre-computed vectors.

🙏

Source & Thanks

Created by Qdrant. Licensed under Apache 2.0.

qdrant — ⭐ 22,000+

Thanks to the Qdrant team for bringing vector search to AI agents.

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