mcp_server

The RunPod Python Code Execution with MCP project is designed to facilitate the execution of Python code by AI assistants on RunPod's serverless infrastructure. It consists of a RunPod Serverless API for executing Python code and an MCP server that interfaces with the RunPod API, providing a standardized interface for AI assistants. The system architecture involves an AI assistant, an MCP server, and the RunPod API. The project includes steps for setting up the RunPod serverless environment, deploying Docker images, and configuring the MCP server. It also outlines the interaction sequence between components, security considerations, troubleshooting tips, and advanced configuration options. The serverless approach is chosen for its advantages in resource management and simplicity, despite some limitations like cold start latency and limited persistent storage.

Features

• Serverless Python Code Execution: Execute Python code on RunPod's serverless infrastructure.
• MCP Server Integration: Connect AI assistants to RunPod using the Model Context Protocol.
• Docker-Based Deployment: Use Docker images to deploy and manage code execution environments.
• Security and Isolation: Code execution occurs in isolated containers with limited execution time.
• Advanced Configuration: Customize execution timeouts, Dockerfile libraries, and error handling.

MCP Tools

• {'execute_python_code': 'Runs Python code on RunPod'}
• {'check_runpod_status': 'Checks the connection status with RunPod'}

Usage with Different Platforms

cline

python
import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.title("Sine Wave")
plt.savefig("sine_wave.png")
print("Generated sine wave plot")