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Weather MCP Server

by nakamurau1

The Weather MCP Server provides weather data from the National Weather Service API, formatted for consumption by Large Language Models. It allows users to retrieve weather forecasts and alerts.

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What is Weather MCP Server?

The Weather MCP Server is a Model Context Protocol (MCP) server that retrieves and formats weather data from the National Weather Service (NWS) API. It's designed to provide clean, structured weather information to Large Language Models (LLMs) like Claude.

How to use Weather MCP Server?

To use the server, first install it by cloning the repository, installing dependencies, and building the project. Then, run the server directly using npm start or integrate it with MCP-compatible clients like Claude Desktop by adding the server configuration to the client's configuration file. You can then query the server using natural language questions about weather forecasts or alerts.

Key features of Weather MCP Server

  • Get weather forecasts by geographic coordinates

  • Get weather alerts by US state

  • Clean, formatted weather data for LLM consumption

  • Integration with Claude and other MCP-compatible clients

Use cases of Weather MCP Server

  • Providing weather information to LLMs for natural language understanding

  • Integrating weather data into conversational AI applications

  • Building weather-aware applications

  • Enhancing LLM responses with real-time weather data

FAQ from Weather MCP Server

What data source does this server use?

This server uses the National Weather Service (NWS) API for weather data.

What geographic area is covered by this server?

The weather data is limited to United States locations due to the NWS API.

Can I get historical weather data?

No, this server only provides current conditions and forecasts, not historical data.

How do I get weather alerts for a specific state?

Use the get_alerts tool with the two-letter US state code as the parameter (e.g., 'CA').

How do I get a weather forecast for a specific location?

Use the get_forecast tool with the latitude and longitude of the location as parameters.