FastAPI Weather Example
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Provides an example Model Context Protocol server implementation for AI agents using FastAPI.
About
This application serves as an example Model Context Protocol (MCP) server, designed to demonstrate how to build robust AI agent tools using FastAPI_MCP. While the sample provides real-time weather information for Japanese cities via Open-Meteo, its core purpose is to offer a customizable boilerplate for developers to integrate their unique business logic into an MCP-compliant server, facilitating seamless interaction with AI agents.
Key Features
- MCP server implementation utilizing the FastApiMCP library for AI agent integration
- Sample endpoint for retrieving real-time weather data for Japanese cities via Open-Meteo
- Built on the high-performance FastAPI framework for efficient API delivery
- 0 GitHub stars
- Customizable logic within endpoints to enable integration of diverse business processes
- Robust error handling for unsupported city inputs and external API connection failures
Use Cases
- Demonstrating how to expose internal services as AI-consumable tools for agent applications
- Learning to develop custom AI agent tools that adhere to the Model Context Protocol
- Providing a template for integrating specific business logic or data sources into an MCP server
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