MCP Server for Qdrant

@Jimmy974

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基本情報

カテゴリ

データベース

トランスポート

stdio

作者

Jimmy974

MCP Server for Qdrant について

概要はまだありません

MCP Server for Qdrant の設定

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": {
    "mcp-server-qdrant-jimmy974": {
      "command": "python",
      "args": [
        "-m",
        "mcp_server_qdrant.main"
      ]
    }
  }
}

MCP Server for Qdrant のツール

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。

概要

What is MCP Server for Qdrant?

MCP Server for Qdrant is a Machine Control Protocol (MCP) server that stores and retrieves text information from a Qdrant vector database. It enables semantic search by generating embeddings via FastEmbed and supports optional metadata attachment. This server is intended for developers building AI‑powered applications that need persistent, searchable memory backed by a vector database.

How to use MCP Server for Qdrant?

Install with pip install mcp-server-qdrant or clone the repository and run make setup. Configure required environment variables (QDRANT_URL, QDRANT_API_KEY, COLLECTION_NAME) in a .env file. Start the server with python -m mcp_server_qdrant.main or make run; alternatively use docker-compose up. Two MCP tools are exposed: qdrant-store (store text with optional metadata) and qdrant-find (semantic search by query).

Key features of MCP Server for Qdrant

Store text information with optional JSON metadata
Semantic search over stored information
FastEmbed integration for text embeddings
Environment-based configuration via .env file
Docker support for containerized deployment
Exposes MCP tools: qdrant-store and qdrant-find

Use cases of MCP Server for Qdrant

Provide persistent memory for AI agents that need to recall past conversations or data
Build a semantic knowledge base that can be queried by natural language
Enable retrieval‑augmented generation (RAG) pipelines with a Qdrant backend
Store and search notes or documents with metadata filters
Prototype MCP‑based applications that require vector storage

よくある質問

What is MCP Server for Qdrant and how is it different from using Qdrant directly?

It provides an MCP interface for Qdrant, allowing any MCP‑compatible client to store and search vectors without writing custom integration code. The server handles embedding generation and tool definition.

How do I install MCP Server for Qdrant?

Install via pip (`pip install mcp-server-qdrant`) or clone the repository and run `make setup` from the source directory.

What dependencies or runtime requirements are needed?

You need a running Qdrant instance and the required environment variables (QDRANT_URL, QDRANT_API_KEY, COLLECTION_NAME). The server uses FastEmbed for embeddings; the model can be configured via EMBEDDING_MODEL.

Where is data stored?

Data is stored in the Qdrant database configured through QDRANT_URL and QDRANT_API_KEY, inside the specified COLLECTION_NAME. No local file storage is used by the server itself.

Does MCP Server for Qdrant support Docker?

Yes. A `docker-compose.yml` is provided; run `docker-compose up` to start the server alongside a Qdrant instance if configured.

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