Weather MCP Server

The Weather MCP Server is a lightweight, Python‑based service that exposes real‑time weather data to AI assistants through the Model Context Protocol. By wrapping the OpenWeatherMap API, it transforms raw meteorological information into a structured format that Claude and other MCP‑compatible clients can consume directly within their conversational context. This eliminates the need for developers to write custom API integration code, allowing assistants to answer location‑specific weather queries instantly.

At its core, the server provides a single tool that returns a comprehensive snapshot of current conditions for any requested city. The response includes temperature (in Celsius), humidity, wind speed, sunrise and sunset times, and a natural‑language weather description. These fields are delivered in metric units by default, ensuring consistency across applications that rely on precise measurements. The simplicity of the interface lets developers embed weather checks in larger workflows—such as travel planning, event scheduling, or IoT dashboards—without juggling authentication tokens or parsing JSON manually.

Key capabilities of the server include:

• Real‑time data retrieval: Each request queries OpenWeatherMap’s live endpoint, guaranteeing up‑to‑date conditions.
• Metric unit standardization: Temperature and wind speed are returned in Celsius and meters per second, respectively, reducing unit conversion errors.
• Rich contextual output: By providing sunrise/sunset times and descriptive text, the server enables assistants to generate more natural responses.
• Environment‑based configuration: The API key is injected via environment variables, keeping credentials secure and simplifying deployment.

Typical use cases span a wide range of scenarios. A travel assistant can ask the server for tomorrow’s forecast in Paris and embed that information into a packing list. A smart home controller might query the current wind speed to decide whether to open windows, while a logistics platform could use sunrise/sunset data to optimize delivery times. Because the server speaks MCP, any Claude‑compatible client—desktop or web—can invoke it with a single command string, keeping the developer’s focus on higher‑level logic rather than low‑level API plumbing.

What sets this MCP server apart is its minimal footprint and zero‑code integration path. Developers can add the service to their Claude Desktop environment with a single Smithery command, after which the assistant is instantly equipped to fetch weather data on demand. This plug‑and‑play model accelerates feature delivery and reduces maintenance overhead, making it an attractive choice for teams that want to enrich AI interactions with reliable external data without investing in bespoke connectors.