What dependencies are required?
The server requires a running Qdrant instance (URL and optional API key), an OpenAI API key for embedding generation, and Python with Poetry for installation.
@davidwynter
基本信息
A simple MCP server to access Qdrant
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"qdrant_mcpserver": {
"command": "python",
"args": [
"-m",
"qdrant_mcpserver.main",
"--server-type",
"fastmcp"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
A dual-protocol server that provides both FastAPI and FastMCP clients for accessing Qdrant knowledge graph operations. It is designed for developers who need to interact with Qdrant vector databases using either REST APIs or the Model Context Protocol.
Install dependencies with Poetry after cloning the repository. Configure environment variables (QDRANT_URL, OPENAI_API_KEY, etc.). Run the server using python -m qdrant_mcpserver.main with an optional --server-type argument to choose between FastAPI (default: FastMCP). Both servers expose the same endpoints.
The server requires a running Qdrant instance (URL and optional API key), an OpenAI API key for embedding generation, and Python with Poetry for installation.
Use the `--server-type` argument when running `main.py`. Accepted values are `fastapi` or `fastmcp`; FastMCP is the default.
FastAPI runs on port 8000 by default; FastMCP runs on port 8080. Both are configurable via the `PORT` and `MCP_PORT` environment variables.
Yes. Both the FastAPI and FastMCP servers expose identical endpoints: `POST /nodes/upsert`, `POST /nodes/search`, `DELETE /nodes`, and `GET /health`.

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