Qdrant MCP 服务器用于向量搜索和语义记忆
An official Qdrant MCP server implementation that provides semantic memory capabilities for AI agents. Enables storing and retrieving information using vector search, acting as a persistent knowledge layer on top of the Qdrant vector database.
Installation
Use the upstream install or setup path that matches your environment:
- docker build -t mcp-server-qdrant .
- docker run -p 8000:8000 \
- npx @smithery/cli install mcp-server-qdrant --client claude
- [](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%2...
Requirements and caveats from upstream:
- ### Using Docker
- necessary when running the server in a Docker container.
- The value of 'metadata' is a Python dictionary with strings as keys. \
Basic usage or getting-started notes:
- This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
- | FASTMCP_SERVER_PORT | Port to run the server on | 8000 |
- ### Using uvx
- Source: https://github.com/1991513ccie-png/skills
- Extracted from upstream docs: https://raw.githubusercontent.com/qdrant/mcp-server-qdrant/HEAD/README.md
