Weather MCP Server

MCP Weather is a lightweight, Python‑based Model Context Protocol (MCP) server that bridges AI assistants with real‑time meteorological data from the National Weather Service (NWS). By exposing two focused tools—weather alerts and weather forecasts—it lets conversational agents answer user questions about current conditions or impending hazards without leaving the chat interface. This eliminates the need for developers to write custom API wrappers, enabling rapid deployment of weather‑aware capabilities in any AI application that supports MCP.

The server’s core value lies in its simplicity and reliability. It translates a user’s request into an NWS API call, formats the response in plain text, and returns it to the assistant. Because the output is already cleaned and human‑readable, developers can embed it directly in dialogue or pass it to downstream NLP pipelines. The alert tool accepts a two‑letter state code and streams all active warnings (e.g., tornado, flood) for that region. The forecast tool takes latitude and longitude, delivering a detailed multi‑hour outlook that includes temperature, precipitation probability, wind speed, and more. This granularity supports both casual inquiries (“What’s the weather in Seattle?”) and safety‑critical scenarios (“Is there a severe storm coming to Denver?”).

Key capabilities include:

• State‑level alert retrieval: Quickly list all active warnings for any U.S. state.
• Location‑specific forecasting: Obtain a structured forecast by geographic coordinates, ideal for mobile or IoT integrations.
• Seamless MCP integration: Exposes tools through the MCP framework, allowing any Claude‑style assistant to invoke them with a simple command.
• Formatted output: Returns concise, easy‑to‑read text that can be rendered in chat or forwarded to other services.

Typical use cases span a wide spectrum: weather‑aware travel assistants can suggest routes that avoid severe storms; emergency response bots can alert users to imminent hazards; smart home systems can adjust HVAC settings based on upcoming forecasts. In each scenario, the MCP Weather server acts as a single point of truth for meteorological data, reducing latency and simplifying maintenance.

What sets MCP Weather apart is its adherence to the MCP standard while providing a niche, high‑value data source. The server requires minimal dependencies (Python 3.13+, , and the MCP CLI package) and can be deployed in a container or serverless environment. Its design encourages rapid prototyping: developers only need to add the tool’s URL to their assistant configuration, and the AI can start issuing weather queries instantly. This plug‑and‑play nature accelerates feature rollouts and ensures that AI assistants remain up to date with the latest weather conditions, enhancing user safety and experience.