RunPod Python Code Execution with MCP
by fulong98
This project enables AI assistants to execute Python code on RunPod infrastructure using the Model Context Protocol (MCP). It consists of a RunPod Serverless API for code execution and an MCP server for standardized AI assistant interaction.
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What is RunPod Python Code Execution with MCP?
This project provides a system for AI assistants to execute Python code on RunPod's serverless infrastructure. It utilizes the Model Context Protocol (MCP) to create a standardized interface between the AI assistant and the RunPod API.
How to use RunPod Python Code Execution with MCP?
The setup involves deploying a Docker image to RunPod Serverless, configuring an MCP server with the RunPod endpoint ID and API key, and then using the MCP server to execute Python code from an AI assistant like Cline. Detailed steps for both RunPod Serverless and MCP server setup are provided in the README.
Key features of RunPod Python Code Execution with MCP
Secure code execution in isolated containers
Standardized interface for AI assistants via MCP
Leverages RunPod serverless infrastructure for scalability and cost-effectiveness
Supports common data science libraries
Error handling and timeout mechanisms
Use cases of RunPod Python Code Execution with MCP
Executing Python code snippets from AI assistants
Generating plots and visualizations
Performing data analysis tasks
Integrating AI assistants with external Python libraries
Automating tasks using Python code triggered by AI assistants
FAQ from RunPod Python Code Execution with MCP
What is the Model Context Protocol (MCP)?
What is the Model Context Protocol (MCP)?
MCP is a standardized protocol for AI assistants to interact with external tools and services.
Why use RunPod Serverless instead of creating full pods?
Why use RunPod Serverless instead of creating full pods?
RunPod Serverless offers advantages such as easier log access, simplified connection management, and cost-effective resource utilization.
What are the prerequisites for setting up the RunPod Serverless API?
What are the prerequisites for setting up the RunPod Serverless API?
You need Docker installed, a RunPod account with an API key, and basic knowledge of Docker and Python.
How do I configure the MCP server?
How do I configure the MCP server?
You need to install the required Python packages (mcp, requests), create a configuration file with your RunPod API key and endpoint ID, and then run the server.
What are some security considerations?
What are some security considerations?
The code execution happens in an isolated container, and execution time is limited. It's recommended to implement additional security measures for production use.