mcp-server-qdrant: A Qdrant MCP server

Name: mcp-server-qdrant: A Qdrant MCP server
Author: qdrant

@qdrant

代码仓库

关于 mcp-server-qdrant: A Qdrant MCP server

An official Qdrant Model Context Protocol (MCP) server implementation

#mcp #cursor #semantic-search #claude #windsurf #llm #mcp-server

基本信息

分类

数据库

许可证

Apache-2.0 license

运行时

python

传输方式

stdio

发布者

qdrant

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-qdrant": {
      "command": "uvx",
      "args": [
        "mcp-server-qdrant"
      ]
    }
  }
}

工具

`information` (string): Information to store

Confirmation message

`query` (string): Query to use for searching

Information stored in the Qdrant database as separate messages

概览

What is mcp-server-qdrant?

mcp-server-qdrant is an official Model Context Protocol server for Qdrant, the vector search engine. It acts as a semantic memory layer that enables LLM applications to store and retrieve information using vector embeddings. It is built for developers integrating AI tools with persistent, searchable memory.

How to use mcp-server-qdrant?

Configure the server via environment variables (e.g., QDRANT_URL, COLLECTION_NAME, EMBEDDING_MODEL). Run it with uvx mcp-server-qdrant or via Docker. The server exposes two tools: qdrant-store to store information and qdrant-find to retrieve relevant information from Qdrant. It supports stdio, sse, and streamable-http transports.

Key features of mcp-server-qdrant

Semantic memory layer on top of Qdrant vector search.
Store and retrieve information with optional metadata.
Automatic collection creation if it does not exist.
Configurable embedding model (FastEmbed models only).
Works with any MCP-compatible client (Claude Desktop, Cursor, VS Code, etc.).
Customizable tool descriptions via environment variables.

Use cases of mcp-server-qdrant

Give LLM applications persistent, searchable memory for conversations or knowledge.
Store and retrieve code snippets with semantic search in Cursor or Windsurf.
Enable Claude Code to search over existing codebases semantically.
Build a personal knowledge base that AI assistants can query.
Share a remote semantic memory server across a team using SSE transport.

FAQ from mcp-server-qdrant

What are the main tools provided?

The server provides qdrant-store to store information (with optional JSON metadata) and qdrant-find to retrieve relevant information based on a query.