Weather MCP Server

Overview

The Mcp Server Weather Js is a lightweight Node.js implementation of the Model Context Protocol (MCP) that exposes real‑time weather data for locations within the United States. By turning a simple HTTP server into an MCP provider, it allows AI assistants such as Claude to query weather conditions and alerts through a standardized tool interface. This eliminates the need for custom API integrations on the client side, enabling developers to add weather functionality with minimal effort.

Problem Solved

Developers building AI‑powered applications often face the challenge of incorporating external data sources while keeping their assistants stateless and secure. Without a dedicated MCP server, each AI client would need to embed API keys or custom logic for weather services. The Weather MCP server centralizes authentication, request validation, and data formatting behind a single endpoint that any compliant AI assistant can call. This reduces boilerplate code, enforces consistent error handling, and keeps sensitive credentials out of the client environment.

What It Does

The server offers two primary tools:

• – Retrieves current weather alerts for a specified U.S. state using its two‑letter code (e.g., or ).
• – Returns a short‑term forecast for any latitude/longitude pair within the United States.

Both tools expose simple JSON schemas that define required inputs and expected outputs, allowing an AI assistant to generate natural‑language queries (e.g., “Tomorrow's weather in Palo Alto?”) and automatically translate them into structured tool calls. The server handles the translation to the underlying weather API, aggregates results, and returns them in a format that adheres to MCP’s context model.

Key Features & Capabilities

• Standardized Tool Interface – Implements MCP’s tool specification, ensuring seamless discovery and invocation by any MCP‑compatible client.
• State‑Level Alerts – Provides up‑to‑date emergency and advisory information for entire states, useful for safety‑critical applications.
• Location Precision – Supports latitude/longitude inputs, enabling granular forecasts for cities, neighborhoods, or specific coordinates.
• Open‑Source & MIT Licensed – The repository is published on npm (), making it easy to deploy via . Its permissive license encourages reuse and modification in commercial projects.

Real‑World Use Cases

• Travel Planning Apps – Deliver instant weather summaries for destinations or itineraries.
• Emergency Response Systems – Fetch alerts for affected regions and surface them in chat or notification channels.
• Smart Home Assistants – Provide contextual weather updates that influence HVAC settings or lighting schedules.
• Educational Tools – Allow students to ask weather‑related questions and receive structured answers powered by the server.

Integration into AI Workflows

Integrating this MCP server with an AI assistant involves adding a single entry to the (or equivalent MCP client configuration). Once registered, the assistant automatically discovers the and tools during conversation. The assistant can then prompt users for missing parameters, execute the tool call, and present the returned data in a conversational format. Because MCP handles the entire request lifecycle—including authentication, rate limiting, and error handling—the developer can focus on higher‑level business logic rather than plumbing.

Standout Advantages

• Zero Client‑Side Secrets – All API keys remain on the server, mitigating exposure risk.
• Rapid Deployment – Published as an npm package and runnable with a single command, enabling quick prototyping.
• Extensibility – The modular design allows additional tools (e.g., hourly forecasts, historical data) to be added with minimal changes.
• Community‑Supported – Built on the official MCP Quickstart, ensuring compatibility with future protocol updates and a growing ecosystem of tools.

In summary, the Mcp Server Weather Js transforms raw weather data into an AI‑friendly service, streamlining the integration of real‑time environmental information into conversational applications.