Weather Data Access
Connects a Python client application with a weather data server, allowing users to query weather information using natural language.
About
This project establishes a client-server architecture leveraging the Model Context Protocol (MCP) to provide weather data access through natural language queries. The Python client interacts with a server that fetches weather alerts and forecasts from the National Weather Service API, using Google's Gemini AI for processing user input. It offers functionalities like querying weather alerts for US states and retrieving detailed forecasts for specific locations, designed to practice and understand how MCP functions.
Key Features
- Queries weather alerts for US states using state codes.
- Includes robust error handling for various potential issues.
- Implements a client-server architecture using Model Context Protocol (MCP).
- Offers a natural language interface powered by Google's Gemini AI.
- Retrieves detailed weather forecasts for specific locations using latitude and longitude.
- 5 GitHub stars
Use Cases
- Getting detailed weather forecasts for specific locations.
- Querying weather alerts for US states.
- Practicing with and understanding the Model Context Protocol (MCP).
Sponsored