# Qdrant (MCP Server)

> Semantic memory store and retrieval backed by a Qdrant vector database

- Type: MCP server
- Publisher: Qdrant (official)
- Categories: AI & ML, Development, Data & Analytics
- Website: https://github.com/qdrant/mcp-server-qdrant
- Source: https://github.com/qdrant/mcp-server-qdrant
- Transport: stdio
- Launch command: uvx mcp-server-qdrant
- Sign-in: api-key
- Environment variables: QDRANT_URL, QDRANT_API_KEY, COLLECTION_NAME
- GitHub stars: 1542
- Listed: 2024-12-02

## About

Qdrant's official MCP server acts as a semantic memory layer, storing information with optional metadata and retrieving it via vector search. It runs via uvx against a remote Qdrant URL or local path, using FastEmbed models by default, and supports stdio, SSE and streamable HTTP.

## Install

1. **Add the server to Codex**

   Codex CLI:

   ```bash
   codex mcp add qdrant --env QDRANT_URL=YOUR_QDRANT_URL --env QDRANT_API_KEY=YOUR_QDRANT_API_KEY --env COLLECTION_NAME=YOUR_COLLECTION_NAME -- uvx mcp-server-qdrant
   ```

   config.toml:

   ```toml
   [mcp_servers.qdrant]
   command = "uvx"
   args = ["mcp-server-qdrant"]
   
   [mcp_servers.qdrant.env]
   QDRANT_URL = "YOUR_QDRANT_URL"
   QDRANT_API_KEY = "YOUR_QDRANT_API_KEY"
   COLLECTION_NAME = "YOUR_COLLECTION_NAME"
   ```

   Agents SDK:

   ```python
   from agents import Agent, Runner
   from agents.mcp import MCPServerStdio
   
   async with MCPServerStdio(
       name="Qdrant",
       params={"command": "uvx", "args": ["mcp-server-qdrant"]},
   ) as server:
       agent = Agent(name="Assistant", mcp_servers=[server])
       result = await Runner.run(agent, "Remember that our staging DB is on port 5433")
       print(result.final_output)
   ```

## Tools

- `qdrant-store`
- `qdrant-find`

## Example prompts

- "Remember that our staging DB is on port 5433"
- "Find what I stored about the deployment process"
- "Store this code snippet with the description 'retry with backoff'"

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