mcpField
M
MCP Weather Server

MCP Weather Server

Local
github.com/aditi-joshi-uscMedia & Design0Updated 83w ago

About MCP Weather Server

MCP Weather Server is a TypeScript and Node.js MCP server for weather information from the National Weather Service API. It provides US state alerts and coordinate-based forecasts for MCP clients.

Key features

  1. 01Current alerts by US state code
  2. 02Forecasts by latitude and longitude
  3. 03Input validation with Zod

Use cases

  1. 01Check weather alerts for a US state
  2. 02Retrieve a forecast for a location

MCP support

Exposes weather-alert and forecast tools to MCP clients, including Claude Desktop and Cursor.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP Weather Server

A lightweight TypeScript + Node.js server built using the Model Context Protocol (MCP) to provide real-time weather alerts and forecast data using the National Weather Service API.

This is my first MCP tool, integrated with Claude Desktop and Cursor, and designed to demonstrate how AI tools can consume external APIs via tool invocation.


Features

get-alerts

Returns current weather alerts for a given US state code (e.g., "CA" for California).

get-forecast

Returns a detailed weather forecast based on geographic coordinates (latitude, longitude).


🛠️ Tech Stack

TechnologyUsage
TypeScriptType-safe development
Node.jsBackend runtime
MCP SDKTool communication with Claude/Cursor
ZodInput schema validation
NWS APIExternal weather data

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.