mcp-server-qdrant
An official Qdrant Model Context Protocol (MCP) server implementation
The Qdrant MCP Server lets your AI use a Qdrant vector store for "memory" and semantic retrieval — stored content is recalled by semantic similarity, not exact match.
For example, "store these technical notes and later find related ones by meaning" — the AI uses this server to write vectors and recall semantically. This is Qdrant's official server.
What it does for you
Connects your AI to Qdrant — semantic search and vector collection management, a memory backend for RAG apps.
Capabilities
- Semantic similarity search
- Store / query vectors
Once installed, try asking your AI
Say this directly in Claude / Cursor or any client with this server connected
- ›“Find the passages most relevant to 'refund policy' in the knowledge base”
Why use it
Good for: developers building RAG, giving the AI long-term memory, or needing semantic search.
Built by Qdrant officially; its health and maintenance data is shown below.
Before you connect
Install / Connect
# No npm package — install from source.
# See the repo's README for the exact command:
# https://github.com/qdrant/mcp-server-qdrantFollow the official README; env and args vary per server.
🛠️ Maintenance weight 30% · 51 pts
📊 Adoption weight 25% · 57 pts
✅ Usability weight 20% · 20 pts
❤️ Health & community weight 25% · 59 pts
Sources: GitHub API · updated 2026-08-19
90-day trend
⭐ stars +0%
Frequently Asked Questions
Do I need to run Qdrant myself?
You need an accessible Qdrant instance (self-hosted or cloud) for the server to read/write.
How is it different from a normal database?
It retrieves by vector similarity — good for "find semantically similar content," not exact SQL queries.
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