DataRobotをChatGPT、Claude、Geminiに接続

• 01 / 03モデルデプロイメントの監査方法
• 02 / 03モデルの比較方法
• 03 / 03データセットの検証方法

最速日

684ms

最遅日

1054ms

14日間の傾向

遅延傾向+8%

DataRobot 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_WX1YGmPzxHEF3itbazDacfMS9v73zZdwIlCWtf4z/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 — DataRobot capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "datarobot-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_WX1YGmPzxHEF3itbazDacfMS9v73zZdwIlCWtf4z/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？ DataRobot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

  データサイエンティスト

  チャット画面から離れることなく、モデルのパフォーマンス監視やプロジェクト間のトレーニングメトリクスの比較を行えます。

• 02

  MLエンジニア

  自然言語を使用して、デプロイメントの監査やAI構成のリアルタイムな検証が可能です。

• 03

  データプラットフォームチーム

  組織全体のプロジェクト横断的なデータセットの使用状況や、モデルのメタデータを監視できます。

• 01

  How do I connect DataRobot to my AI client?

  You subscribe to the MCP on Vinkius and enter your DataRobot API Key and Endpoint URL. Once connected, you can use it in any MCP-compatible client.

• 02

  Which AI clients can use this MCP?

  You can use this MCP with Claude, Cursor, Windsurf, VS Code, or any other MCP-compatible client.

• 03

  Can I check model performance through the chat?

  Yes, your agent can retrieve raw training metrics and specific model properties directly.

• 04

  Where do I find my DataRobot API credentials?

  You can find your API Key and Endpoint URL in your DataRobot account under Profile and then API Keys.

• 05

  Does this MCP support deployment management?

  Yes, you can list deployments and trace the configurations of nodes deployed into the cloud.

• 06

  Can my agent list all models within a specific DataRobot project?

  Yes. Use the 'list_models' capability and provide the project ID. The agent will enumerate the explicit bounded layers and AI configurations stored directly in the DataRobot platform, allowing you to compare models through the chat.

• 07

  How do I retrieve training metrics for a specific model via chat?

  Provide the project ID and model ID to the 'get_model' capability. Your agent will retrieve the discrete logical properties and natively export raw training metrics within your mapped ML structures accurately.

• 08

  Can I monitor active cloud deployments through the agent?

  Absolutely. Use the 'list_deployments' capability. Your agent will intercept precise global configurations tracing executed DataRobot nodes deployed natively into scalable clouds, giving you real-time visibility into your production AI.

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