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
基本情報
A simple MCP server to access Qdrant
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"qdrant_mcpserver": {
"command": "python",
"args": [
"-m",
"qdrant_mcpserver.main",
"--server-type",
"fastmcp"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
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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