MCP Weather Example

What is mcp-weather-example

mcp-weather-example is a practical demonstration of using the Model Context Protocol (MCP) to query weather information. Developed by andre-s-gomes, this server showcases how AI systems can securely connect and retrieve real-time weather data through a client-server architecture. The protocol allows AI assistants to access external data sources like OpenWeatherMap, providing timely and accurate weather updates. By leveraging MCP, this server facilitates a seamless interaction between AI systems and weather data services.

How to Use mcp-weather-example

To start using the mcp-weather-example, you'll need to prepare your environment and execute a few straightforward steps:

1. Prerequisites: Ensure you have Python 3 installed on your system. Additionally, install the requests library by running:

   pip3 install requests
   

   You'll also need an API key from OpenWeatherMap to access weather data.

2. Setup:

   • Clone the repository to your local machine.
   • Navigate into the directory of the cloned repository.

3. Configuration:

   • Open the weather_server.py file and insert your OpenWeatherMap API key where indicated.

4. Execution:

   • Start the server by running:

     python3 weather_server.py
     

   • Test the client-side functionality by executing:

     python3 mcp_client.py
     

This setup will allow you to simulate how an AI system can request and receive weather updates using the MCP framework.

Key Features of mcp-weather-example

• MCP Integration: Utilizes the Model Context Protocol to enable secure and standardized connections between AI systems and external data like weather APIs.
• Real-Time Data Access: Provides real-time weather updates by integrating with OpenWeatherMap, allowing AI systems to deliver accurate and timely information.
• Simple Configuration: Requires minimal setup with a straightforward process of adding an API key and running the server and client scripts.
• Educational Tool: Acts as a practical example for developers looking to understand and implement MCP in their own projects.
• Open-Source Nature: Being open-source, it allows users to explore and modify the code, enhancing learning and customization opportunities.

The mcp-weather-example is an excellent resource for developers and AI enthusiasts interested in exploring the capabilities of MCP and its applications in real-world scenarios like weather data retrieval. Whether you're a beginner or an experienced developer, this tool offers valuable insights into the integration of AI systems with external data sources.