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MCP Server

@fulong98

リポジトリ

基本情報

カテゴリ

その他

トランスポート

stdio

作者

fulong98

MCP Server について

概要はまだありません

#mcp-server#server#backend

MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-server-fulong98": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "yourusername/runpod-python-executor:latest",
        "."
      ]
    }
  }
}

MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is mcp_server?

mcp_server enables AI assistants (like Cline) to execute Python code on RunPod serverless infrastructure using the Model Context Protocol (MCP). It consists of a RunPod Serverless API that runs the code and an MCP server that provides a standardized interface for the assistant.

How to use mcp_server?

After deploying a RunPod serverless endpoint and obtaining its ID, install the Python dependencies (pip install mcp requests), set the RUNPOD_API_KEY and RUNPOD_ENDPOINT_ID environment variables, and run the MCP server script. Connect your AI assistant (e.g., Cline) to the MCP server, then use the execute_python_code tool to run code and check_runpod_status to verify connectivity.

Key features of mcp_server

  • Execute Python code on RunPod serverless infrastructure
  • Standardized MCP interface for AI assistants
  • Returns stdout, stderr, and exit code
  • Pre‑built Docker image with common data science libraries
  • Isolated container execution for security

Use cases of mcp_server

  • Run Python scripts from an AI assistant without local resources
  • Generate plots and perform data analysis in a serverless environment
  • Offload computational tasks to the cloud via a natural language interface
  • Execute code that requires libraries not available on the assistant’s host

よくある質問

1

Why use serverless instead of creating full pods?

RunPod does not provide a direct API to retrieve logs from pods, making it hard to capture output. Serverless functions charge only for actual execution time and avoid persistent connection complexity.

2

What are the limitations of the serverless approach?

Cold start latency occurs on first execution. Each run starts with a fresh environment (no persistent storage). Resource constraints (CPU, memory, disk) are tighter than dedicated pods, and GPU access is more limited and typically more expensive.

3

Is my code execution secure?

Code runs in an isolated container with a limited execution time to prevent resource abuse. Additional security measures should be implemented for production use.

4

What should I do if code execution times out?

Your code might be too complex or contain an infinite loop. Check the execution timeout setting (`MAX_EXECUTION_TIME`) and ensure your logic terminates promptly.

5

How can I test the RunPod endpoint before using the MCP server?

Use a curl command like: `curl -X POST https://api.runpod.io/v2/{endpoint_id}/runsync -H 'Content-Type: application/json' -H 'Authorization: Bearer YOUR_RUNPOD_API_KEY' -d '{"input": {"code": "print(\"Hello, World!\")"}}'`.

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