Weather API MCP Server

Overview

The Mcp Weatherapi server transforms a conventional Spring Boot weather service into an AI‑ready endpoint that can be queried by Claude or other Model Context Protocol clients. By exposing a simple, well‑documented REST API that returns current weather data in JSON format, the server allows AI assistants to incorporate real‑time meteorological information into conversations or workflows without needing direct access to external weather APIs. This capability is essential for developers building context‑aware assistants that must respond accurately to location‑based questions or automate tasks dependent on weather conditions.

At its core, the server offers a single endpoint. Internally it leverages Spring WebClient to call the external WeatherAPI.com service, handling authentication via an API key that is configured in or . The response is parsed into clean Data Transfer Objects (DTOs) and re‑serialized to JSON, ensuring that downstream AI clients receive a consistent structure. The use of reactive programming patterns means the service can scale to handle many concurrent requests, which is particularly useful when an AI assistant must serve multiple users simultaneously.

Key features include:

• Language agnosticism – The server is built with Java 21 and Kotlin, making it easy to integrate into JVM‑based projects or expose its endpoints to any language that can perform HTTP requests.
• Configurable integration – API base URL, endpoint path, and key are externalized in configuration files, allowing teams to switch providers or update credentials without code changes.
• Logging and observability – Weather data is logged for debugging, enabling developers to trace requests and diagnose issues quickly.
• Modular design – DTOs separate concerns between the external API schema and the internal representation, facilitating future extensions such as additional weather metrics or forecast endpoints.

Typical use cases involve AI assistants that answer user queries like “What’s the weather in Paris?” or automate actions such as adjusting indoor climate control based on outdoor conditions. In a customer support bot, the MCP server can provide up‑to‑date weather context when troubleshooting climate‑related product issues. For developers, the server’s clean JSON output can be directly fed into Claude’s tool execution pipeline, allowing the assistant to make real‑time decisions or provide enriched responses without embedding API keys in client code.

What sets this MCP server apart is its blend of simplicity and scalability. By wrapping a third‑party weather API in a lightweight, reactive Spring Boot application, it offers AI developers a reliable, secure, and easily configurable data source. The server’s architecture ensures that the assistant can trust the accuracy of weather information while keeping sensitive credentials isolated from the AI model, thereby maintaining both functionality and security in production deployments.