Mcp Lightning Lesson
What is Mcp Lightning Lesson
mcp-lightning-lesson is a Python project that demonstrates how to build an agentic AI application using OpenAI GPT models and MCP (Model Context Protocol) agents to fetch and process real-world data.
Use cases
Use cases include developing applications that provide real-time weather updates, integrating AI agents for data analysis, and creating tools that require current time information.
How to use
To use mcp-lightning-lesson, clone the repository, set up a virtual environment, configure your OpenAI API key in the .env file, and run the main script using ‘uv run python main.py’.
Key features
Key features include custom agent implementation using OpenAI GPT models, integration with MCP server for data fetching, an example function tool for current time retrieval, and structured JSON output.
Where to use
mcp-lightning-lesson can be used in fields such as AI development, data processing, and real-time information retrieval, particularly in applications requiring weather forecasting or similar data.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Mcp Lightning Lesson
mcp-lightning-lesson is a Python project that demonstrates how to build an agentic AI application using OpenAI GPT models and MCP (Model Context Protocol) agents to fetch and process real-world data.
Use cases
Use cases include developing applications that provide real-time weather updates, integrating AI agents for data analysis, and creating tools that require current time information.
How to use
To use mcp-lightning-lesson, clone the repository, set up a virtual environment, configure your OpenAI API key in the .env file, and run the main script using ‘uv run python main.py’.
Key features
Key features include custom agent implementation using OpenAI GPT models, integration with MCP server for data fetching, an example function tool for current time retrieval, and structured JSON output.
Where to use
mcp-lightning-lesson can be used in fields such as AI development, data processing, and real-time information retrieval, particularly in applications requiring weather forecasting or similar data.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
Build Your First Agentic AI App with MCP
Maven Lightning Lesson
A Python project demonstrating the use of OpenAI GPT models and MCP (Model Context Protocol) agents to fetch and process real-world data.
Overview
This project provides an example of using a custom agent to retrieve weather forecasts for Hintertux, Austria, by fetching data from the meteoblue.com website. It leverages the openai-agents library and its capabilities to run and expose MCP Servers.
Features
- Custom agent implementation using OpenAI GPT models
- Integration with MCP server for data fetching
- Example function tool (
get_time) for current time retrieval - Returns structured JSON output
Requirements
- Python 3.13+
- OpenAI API key
Installation
- Clone the repository:
git clone ... cd mcp-lightning-lesson - Create a venv using uv:
uv venv - Copy the example environment file and add your OpenAI API key:
cp .env.example .env # Edit .env and set your OPENAI_API_KEY
Usage
Run the main script:
uv run python main.py
The agent will:
- Fetch the weather forecast for Hintertux, Austria
- Get the current time
- Return the results as a JSON payload
Project Structure
main.py– Main application logicpyproject.toml– Project dependencies and metadata.env.example– Example environment variable file
Dependencies
- openai-agents >= 0.0.15
- python-dotenv >= 1.1.0
Frontend
Coming soon!
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
