QdrantをChatGPT、Claude、Geminiに接続

• 01 / 03ベクトルデータセットのクレンジング方法
• 02 / 03RAGのデバッグ方法
• 03 / 03コレクションの管理方法

最速日

757ms

最遅日

1106ms

14日間の傾向

遅延傾向+23%

Qdrant Connector

準備完了です。MCPクライアントを選択し、セットアップ手順に従ってください。

コネクタリンクhttps://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcpコピー

Claude Desktop

以下の手順で数秒で接続できます。

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — Qdrant capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "qdrant-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

各クライアントの手順ガイドはこちらです。 接続方法

Claude向けのガイド付きセットアップ？ QdrantをClaudeや他のAIに設定する

このConnectorが対応するすべてのAIクライアントを見る ↑

• 01

  AI/MLエンジニア

  RAGアプリケーションの構築中に、コンソールから直接埋め込み空間をクエリできます。

• 02

  データサイエンティスト

  Jupyter Notebookを起動することなく、ライブインデックスのペイロード確認や距離パラメータのテストが可能です。

• 03

  バックエンド開発者

  ベクトルクラスターの設定管理や、不要なデータセットの効率的なクリーンアップを行えます。

• 01

  Does this work with self-hosted Qdrant?

  Yes, this MCP connects to both Qdrant Cloud and self-hosted instances.

• 02

  Which AI clients can I use with this MCP?

  You can use any MCP-compatible client, including Claude, Cursor, and Windsurf.

• 03

  Can I perform similarity searches?

  Yes, you can perform nearest neighbor similarity searches by providing a JSON array of floats to the search capability.

• 04

  Is deleting data permanent?

  Yes, the delete capability performs an irreversible action on the specified points.

• 05

  How do I see my data payloads?

  You can use the scroll capability to paginate through points and view their associated payloads.

• 06

  How do I find my Qdrant URL and API Key?

  For Qdrant Cloud: Go to the Qdrant Cloud Console, select your cluster to open the Cluster Detail Page. The endpoint will be displayed there (e.g., xyz.us-east4-0.gcp.cloud.qdrant.io), and you can generate Database API Keys underneath it (they start with eyJhb). For Self-hosted: Provide your custom URL and the static custom key you defined in your config.yaml.

• 07

  Can my AI use this for a RAG architecture directly?

  Yes contextually, but practically your agent acts as the database debugger. It can formulate vector arrays to query search_points, retrieving identical payload structures. It's meant for the engineer building the RAG, helping you inspect distances and debug faulty retrieval mechanisms mid-code.

• 08

  Does it support deleting vectors?

  Yes. If an embedding got corrupted or references dropped articles, use the delete capability. Pass the collection name and the list of specific IDs. Qdrant handles the mutation instantly and updates the index without rebuilding.

• 09

  What if I have millions of points?

  Instead of overloading your chat context, instruct your agent to use the count capability to grasp the scale, and the scroll capability with a small limit constraint (e.g., 5-10 records at a time). This paginates large bodies cleanly when analyzing index health.

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