Modelbit ML Model DeploymentsをAIに接続

• 01 / 03モデルのテストを自動化する方法
• 02 / 03推論を実行する
• 03 / 03推論を実行する

最速日

681ms

最遅日

862ms

14日間の傾向

安定+4%

Modelbit 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_U02S7R70GaoTP6dztOIIMDtmgDyuN2GTH3Or66r0/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 — Modelbit capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "modelbit-ml-model-deployments-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_U02S7R70GaoTP6dztOIIMDtmgDyuN2GTH3Or66r0/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？ Modelbit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

  AIチャットインターフェース上で、モデルの出力結果を直接テストおよび検証できます。

• 02

  MLエンジニア

  独自の連携コードを構築することなく、プロダクションモデルをAI駆動のワークフローに統合できます。

• 03

  プロダクトチーム

  独自の機械学習ロジックに依存するAI機能のプロトタイプを迅速に開発できます。

• 01

  How do I connect my Modelbit models to my AI client?

  You subscribe to the MCP on Vinkius and provide your Modelbit Workspace name. Once connected, your client can use the capabilities to call your deployments.

• 02

  What kind of models can I use with this MCP?

  You can use any deployed models in your Modelbit workspace, including those built with Python, Scikit-learn, or PyTorch.

• 03

  Can I use specific versions of my models?

  Yes, you can specify exact model versions or tags like 'latest' to ensure your agent uses the correct deployment.

• 04

  Do I need to write code to call my models?

  No, once the MCP is connected, your AI agent uses the get_inference capability to handle the communication for you.

• 05

  Is it possible to use private deployments?

  Yes, you can provide your Modelbit API Key during setup to access your private deployments.

• 06

  Can I specify which version of a model to use for inference?

  Yes. When using the get_inference capability, you can provide an optional version string (e.g., 'v1', 'latest', or a specific tag) to target a precise deployment.

• 07

  What format should the input data be in?

  The get_inference capability accepts a data parameter which should be a JSON object or array, matching the input schema expected by your Modelbit deployment.

• 08

  Is an API Key required for all models?

  The MODELBIT_API_KEY is optional. It is only required if your Modelbit deployment is private. Public deployments only require the MODELBIT_WORKSPACE name.

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