PaperspaceをChatGPT、Claude、Geminiに接続

• 01 / 03GPU使用状況を監査する
• 02 / 03デプロイログのデバッグ方法
• 03 / 03ノートブックのリソース監視方法

最速日

653ms

最遅日

957ms

14日間の傾向

改善傾向-12%

Paperspace 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_w4nfco02fghsGPeqz307rO7rx1srVWdx33BhZToR/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 — Paperspace capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "paperspace-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_w4nfco02fghsGPeqz307rO7rx1srVWdx33BhZToR/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？ Paperspace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開発者

  チャットインターフェースから、重いモデルへのGPU割り当て状況や制限を即座に確認できます。

• 02

  インフラ運用担当者

  切り離されたデプロイメントをフェッチし、どのコンテナAPIがネイティブにアクティブかを確認できます。

• 03

  機械学習研究者

  AIラボのセットアップを追跡し、Jupyterの制限やRAMの境界を即座に調査できます。

• 01

  How do I connect this MCP to my AI client?

  You subscribe via Vinkius and enter your Paperspace API Key. Once connected, you can use it immediately in any MCP-compatible client like Claude or Cursor.

• 02

  What kind of hardware information can my agent see?

  Your agent can see specific machine details including GPU types, memory schemas, and storage constraints for your active instances.

• 03

  Can I manage Jupyter notebooks with this?

  Yes, the MCP allows your agent to query raw Jupyter notebooks attached to Gradient models to check for idle constraints.

• 04

  Is this compatible with my existing deployment workflows?

  Yes, it allows you to check container availability and pull logs for serverless API containers directly through your agent.

• 05

  Does this work with team accounts?

  Yes, you can use it to trace collaborative groupings and check how GPU units map to specific team projects.

• 06

  Are Paperspace Core machines dynamically mapped?

  Yes. The list_machines query returns deeply structured attributes associated exactly with the base compute objects provisioning storage arrays, IPs, and states running natively over Paperspace Core.

• 07

  Can I spin up new Jupyter Gradient instances?

  Currently, this module focuses strictly on dynamic observability. pulling down Notebooks arrays, Teams constraints, and extracting native deploy mapping contexts. Write operations to spin up environments are out-of-scope for read workflows.

• 08

  How do I fetch the resource specs belonging to a specific ID?

  After listing the overall arrays, provide the psxxxxxx ID identifier securely to the get_machine_details extractor to generate raw hardware limitations mapped logically inside that node.

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