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mcp-server-qdrant: A Qdrant MCP server

@qdrant

リポジトリ

基本情報

カテゴリ

データベース

ライセンス

Apache-2.0 license

トランスポート

stdio

作者

qdrant

mcp-server-qdrant: A Qdrant MCP server について

An official Qdrant Model Context Protocol (MCP) server implementation

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

mcp-server-qdrant: A Qdrant MCP server 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": {
      "command": "uvx",
      "args": [
        "mcp-server-qdrant"
      ]
    }
  }
}

mcp-server-qdrant: A Qdrant MCP server のツール

4

`information` (string): Information to store

Confirmation message

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

Information stored in the Qdrant database as separate messages

概要

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.

よくある質問

1

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.

2

What embedding models are supported?

Currently only FastEmbed models are supported. The default model is `sentence-transformers/all-MiniLM-L6-v2`.

3

Can I run the server locally without a remote Qdrant instance?

Yes. Set `QDRANT_LOCAL_PATH` to a local database path; do not set `QDRANT_URL` at the same time.

4

Which transport protocols are available?

The server supports `stdio` (default), `sse`, and `streamable-http`. Use the `--transport` flag to select one.

5

Does the server require an API key for Qdrant?

It is optional. You can set `QDRANT_API_KEY` if your Qdrant server requires authentication.

コメント

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