MongoDB Atlas Vector SearchをAIに接続

• 01 / 03インデックスの構成方法
• 02 / 03MongoDBドキュメントの管理方法
• 03 / 03ベクトル検索の実行方法

最速日

750ms

最遅日

1215ms

14日間の傾向

改善傾向-12%

MongoDB Atlas Vector Search 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_rMRfujNKzotBfoPOFvzesaIZTdVkigbmunZrRwce/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 — MongoDB Atlas Vector Search capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "mongodb-atlas-vector-search-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_rMRfujNKzotBfoPOFvzesaIZTdVkigbmunZrRwce/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？ MongoDB Atlas Vector Search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

  MLエンジニア

  SDKスクリプトを記述することなく、自然な対話を通じてベクトルの関連性をテストし、埋め込みの次元を確認できます。

• 02

  バックエンド開発者

  ワークスペースのターミナルから直接、運用データとベクトル検索の結果を一つのワークフローで管理できます。

• 03

  検索アーキテクト

  複数のAtlas環境にわたる検索インデックスの監査や、コレクションの構成を効率的に監視できます。

• 01

  What AI clients can I use with this MCP?

  You can connect this MCP to any compatible client, including Claude, Cursor, Windsurf, and VS Code.

• 02

  Do I need to write MQL manually?

  No. You can describe the data you want to find or change in plain language, and your agent will handle the MQL execution.

• 03

  Can I manage my search indices?

  Yes. The MCP includes a capability to create and configure Atlas Search indices with custom dimensions.

• 04

  Does this work with standard MongoDB documents?

  Yes. Beyond vector search, you can use standard MQL filters to find, insert, and delete documents.

• 05

  Where is the data hosted?

  The data stays in your MongoDB Atlas cluster. Vinkius simply hosts the MCP that allows your agent to talk to your cluster.

• 06

  Can I manage both vector search and standard data in the same conversation?

  Yes. MongoDB Atlas Vector Search is unified. You can use the search capability for similarity and the find or insert capabilities for standard operational data management using MQL, allowing you to bridge both worlds natively.

• 07

  How do I create a new vector search index through the agent?

  Use the create_index capability by providing the database, collection, and required dimensions (matching your embedding model). Your agent will provision the index infrastructure on Atlas to enable high-speed vector retrieval.

• 08

  Can my agent find specific documents using standard MongoDB query filters?

  Absolutely. Use the find capability with a JSON string representing your MQL filter (e.g. {"status":"active"}). Your agent will execute the Data API request and return the matching documents and their scalar properties securely.

• 

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• 

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