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MCP Server

@fulong98

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

fulong98

关于 MCP Server

暂无概览

#mcp-server#server#backend

MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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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