MCP

A Simple implementation of a command-line tool that provides access to US weather data through a client-server architecture using the Model Context Protocol (MCP) and Google's Gemini AI. Built to practive and understand how MCP works.

Overview

This project connects a Python client application with a weather data server, allowing users to query weather information using natural language. The server communicates with the National Weather Service API to retrieve weather alerts and forecasts.

Features

• Query weather alerts for US states using state codes

• Get detailed weather forecasts for specific locations using latitude and longitude

• Natural language interface powered by Google's Gemini AI

• Client-server architecture using Model Context Protocol (MCP)

Prerequisites

• Python 3.8+

• Node.js (if running JavaScript server)

• Google Gemini API key

Installation

1. Clone the repository:

   git clone https://github.com/Abhinavexists/MCP_Server.git
   cd weather-tool

2. Install uv if you don't have it already:

   pip install uv

3. Create and activate a virtual environment:

   uv venv

   • On Windows: .venv\Scripts\activate

   • On macOS/Linux: source .venv/bin/activate

4. Install dependencies using uv (this project uses uv.lock and pyproject.toml):

   uv pip sync

5. Create a .env file in the project root directory with your Gemini API key:

   GEMINI_API_KEY=your_gemini_api_key_here

Usage

1. Start the client and connect to the weather server:

   python client.py server.py

2. Once connected, you can ask questions about weather information:

   Query: What are the current weather alerts in CA?
   Query: What's the forecast for latitude 37.7749, longitude -122.4194?

3. Type quit to exit the application.

Available Tools

The server provides the following tools:

• get_alerts: Fetches weather alerts for a specified US state (using two-letter state code)

• get_forecast: Retrieves weather forecasts for a specific location (using latitude and longitude)

Project Structure

• client.py: MCP client that connects to the server and processes user queries using Gemini AI

• server.py: MCP server that implements weather data tools and communicates with the National Weather Service API

Error Handling

The application includes robust error handling for:

• Invalid server script paths

• Connection issues with the NWS API

• Invalid or missing data in API responses

Future Improvements

• Add additional weather data endpoints

• Implement caching for frequently requested data

• Add support for location name lookup (instead of requiring lat/long)

• Create a web interface

License

MIT License

Resources

For more information about Model Context Protocol (MCP), refer to the official Claude MCP documentation:

• Claude MCP Documentation