Deploy an MCP Server on Raspberry Pi 5 for AI agent interaction using OpenAI SDK
Introduction
Introduction to Model Context Protocol (MCP) and Python uv package for local AI agents
Set up an MCP server on Raspberry Pi 5
Build and run an AI agent on your development machine
Next Steps
Deploy an MCP Server on Raspberry Pi 5 for AI agent interaction using OpenAI SDK
Who is this for?
This Learning Path is for LLM and IoT developers who want to run and interact with AI agents on edge devices like the Raspberry Pi 5. You'll learn how to deploy a lightweight Model Context Protocol (MCP) server and use the OpenAI Agent SDK to create and register tools for intelligent local inference.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Deploy a lightweight Model Context Protocol (MCP) server on Raspberry Pi 5 for local AI agent execution.
- Use the OpenAI Agent SDK to interact with a local AI agent.
- Design and register custom tools for the agent tasks.
- Learn about uv - a fast, efficient Python package manager for efficient local deployment.
Prerequisites
Before starting, you will need the following:
- A Raspberry Pi 5 with a Linux-based OS installed.
- Familiarity with Python programming and prompt engineering techniques.
- Basic understanding of Large Language Models (LLMs) and how they are used in local inference.
- Understanding of AI agents and the OpenAI Agent SDK (or similar frameworks).
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
uv and bootstrap a FastMCP server with CPU-temperature and weather tools. Then, you’ll expose the server through an ngrok tunnel and configure an agent on a separate Arm Linux machine. Finally, you’ll connect the server to the agent and prompt to retrieve live values from the Pi and external sources.Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
ngrok tunnel.uv on Raspberry Pi to set up the MCP server, and install it on your development machine to initialize and run the agent project.uv init creates a .venv virtual environment and a pyproject.toml file in the project directory. Both files indicate that the project environment is ready.ngrok HTTPS forwarding URL or the Pi’s reachable network address. A correct endpoint lets the agent start without connection errors.