🌦️ Weather MCP Server and Client

Welcome to the Weather Model Context Protocol (MCP) Project! This project provides real-time weather intelligence by integrating with external APIs like the National Weather Service (NWS). It is built using the FastMCP framework and supports both server and client interactions.

🚀 Features

• Weather Alerts: Fetch weather alerts for any US state.
• Weather Forecasts: Get detailed weather forecasts for specific locations.
• Asynchronous Operations: Fully asynchronous for high performance.
• Modular Design: Clean separation of server, client, and utility logic.
• LLM Integration: Summarize weather data using a language model (DistilGPT-2).

📂 Project Structure

• server.py: The main server file that registers tools for fetching weather alerts and forecasts.
• client.py: A command-line client to interact with the server.
• app.py: A Streamlit-based web app for user-friendly interaction.
• agent.py: An advanced client integrating a language model for summarizing weather data.

🛠️ Setup Instructions

1. Clone the Repository:

   git clone <repository-url>
   cd mcp
   

2. Install Dependencies: Ensure Python 3.8+ is installed, then run:

   pip install -r requirements.txt
   

3. Run the Server: Start the FastMCP server:

   python server.py
   

4. Interact with the Client: Use the command-line client or the Streamlit app to interact with the server.

🖥️ Usage

Command-Line Client

1. List Available Tools:

   python client.py list_tools
   

2. Fetch Alerts:

   python client.py get_alerts --state CA
   

3. Fetch Forecasts:

   python client.py get_forecast --latitude 34.05 --longitude -118.25
   

Streamlit Web App

1. Run the app:

   streamlit run app.py
   

2. Use the sidebar to select actions like "List Tools," "Get Alerts," or "Get Forecast."

LLM Integration

1. Summarize weather data using the language model:

   python agent.py interact_with_llm --llm_action get_alerts --state CA
   python agent.py interact_with_llm --llm_action get_forecast --latitude 34.05 --longitude -118.25
   

🎥 Video Demonstration

Watch a quick demonstration of the Weather MCP Project in action:

[Weather MCP Demo]

🌐 External Dependencies

• FastMCP: Framework for building MCP servers and clients.
• httpx: For making asynchronous HTTP requests.
• Streamlit: For building the web app.
• Transformers: For integrating the DistilGPT-2 language model.

📋 Notes

• Ensure the server is running before using the client or web app.
• Follow the asynchronous programming model to avoid blocking operations.

❤️ Acknowledgments

• FastMCP for the server-client framework.
• National Weather Service (NWS) for providing weather data.
• Hugging Face Transformers for the DistilGPT-2 model.

🤝 Contributing

We welcome contributions to the Weather MCP Project! Here's how you can help:

1. Report Bugs: If you encounter any issues, please open an issue on the GitHub repository.
2. Suggest Features: Have an idea for a new feature? Let us know by creating a feature request.
3. Submit Pull Requests: Fork the repository, make your changes, and submit a pull request for review.
4. Improve Documentation: Help us enhance the documentation by fixing typos, adding examples, or clarifying instructions.

Contribution Guidelines

• Follow the project's coding conventions and structure.
• Ensure all new code is covered by tests.
• Use clear and concise commit messages.
• Test your changes thoroughly before submitting.

Enjoy exploring the weather with Weather MCP! 🌤️