EY.ai Weather MCP Server

Technology

Model Context Protocol (MCP) Server built with Python & FastAPI

This is a Model Context Protocol (MCP) server that provides weather data capabilities for AI agents and LLM applications.

Available MCP Tools (2)

This MCP server exposes the following tools through the Model Context Protocol. These tools can be accessed by any MCP-compatible client such as Claude Desktop or through the MCP Inspector for testing.

get_alerts

Get weather alerts for a specific area. Args: state: Two-letter state code (e.g., 'CA', 'NY') zone: Specific zone ID (e.g., 'CAZ006') Returns: JSON string with weather alerts

# Example parameters
{
  "state": "CA"
}

Parameters:

• state (string, optional)

• zone (string, optional)

get_forecast

Get weather forecast for a location. Args: latitude: Latitude coordinate (e.g., 37.7749) longitude: Longitude coordinate (e.g., -122.4194) Returns: JSON string with weather forecast

# Example parameters
{
  "latitude": 37.7749,
  "longitude": -122.4194
}

Parameters:

• latitude (number, required)

• longitude (number, required)

MCP Inspector Testing

Use the MCP Inspector to connect to the streamable MCP endpoint and test tools interactively. The /mcp endpoint is live and ready to accept connections!

Launch MCP Inspector

The MCP Inspector provides a visual interface to test tool calls and inspect server responses. Simply run this command to connect to the streamable HTTP endpoint.

# Launch MCP Inspector (connects to http://localhost:/mcp)
npx @modelcontextprotocol/inspector \
  http://localhost:/mcp

Quick Start

Get started with this Model Context Protocol (MCP) server locally or with Docker. The server provides both a web app and streamable MCP endpoint.

Local Development (Python)

# Install dependencies
make install

# Start server (web app + MCP endpoint on port )
make run

# Open web app
open http://weather-sak-1-cd-dev-1-hphqn.dev-workflow-uat-nov-26-eve.002.eastus2.containers.sbp.eyclienthub.com/

Docker Deployment

# Build Docker image
make docker-build

# Run container (maps port :8080)
make docker-run