mcp_server

Name: mcp_server
Author: fulong98

@fulong98

Repository

About mcp_server

No overview available yet

#mcp-server #server #backend

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

FAQ from mcp_server

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.

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.

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.

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.

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!\")"}}'.