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MCP Weather Server: Simulate & Test Weather APIs Effortlessly - MCP Implementation

MCP Weather Server: Simulate & Test Weather APIs Effortlessly

MCP Weather Server: Your reliable, seamless testbed for US weather APIs! Effortlessly simulate forecasts and experiment with crisp data – perfect for testing!

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Ranked in the top 2% of all AI tools in its category

About MCP Weather Server

What is MCP Weather Server: Simulate & Test Weather APIs Effortlessly?

MCP Weather Server is a specialized Model Context Protocol (MCP) server designed to provide real-time USA weather data to AI assistants like Claude. Built on the National Weather Service API, it enables seamless integration of weather information into conversational AI workflows. This tool simplifies access to critical weather alerts and forecasts, allowing developers and users to test and simulate weather-related interactions without direct API handling.

Key Features of MCP Weather Server: Simulate & Test Weather APIs Effortlessly?

  • Real-Time Weather Alerts: Retrieve active weather warnings for any U.S. state, ensuring users stay informed about severe conditions.
  • Granular Forecasts: Obtain detailed 7-day forecasts using precise latitude/longitude coordinates, supporting hyperlocal weather analysis.
  • MCP Compatibility: Native support for MCP-enabled platforms like Claude Desktop, streamlining AI-driven weather queries.

MCP Weather Server Features

How to Use MCP Weather Server: Simulate & Test Weather APIs Effortlessly?

Deployment follows a two-step process:

  1. Installation: Use pip (`pip install mcp-weather`) or uv (via terminal) to integrate the server into your environment. Developers can also clone the GitHub repository and configure a virtual environment.
  2. Configuration: Add server settings to platform-specific files like cline_mcp_settings.json or claude_desktop_config.json, enabling auto-approval for seamless AI interactions.

Once activated, simply ask questions like: "Are there active tornado warnings in Texas?" or "Show the 48-hour forecast for 37.7749,-122.4194" (San Francisco coordinates).

Use Cases of MCP Weather Server: Simulate & Test Weather APIs Effortlessly?

  • Emergency Preparedness: Developers can test disaster response workflows by simulating extreme weather alerts in specific regions.
  • Smart Home Integration: Enable voice assistants to provide localized forecasts for automated climate control systems.
  • Educational Tools: Create interactive learning modules about weather patterns using real-time API responses.
  • Travel Planning: Power chatbots that analyze weather trends for itinerary adjustments during user conversations.

MCP Weather Server FAQ

FAQ: MCP Weather Server & Testing Best Practices

Q: Does this work with non-MCP platforms?
A: Core functionality requires MCP compatibility, but raw API endpoints can be accessed programmatically for custom use cases.

Q: How often is weather data updated?
A: The underlying National Weather Service API provides near-realtime updates, typically within 5-10 minutes of official alerts.

Q: Can I mock faulty API responses for testing?
A: Yes, developers can override the default behavior using environment variables to simulate network errors or outdated data scenarios.

Q: What coordinate formats are accepted?
A: Both comma-separated pairs (e.g., "40.7128,-74.0060") and objects with lat/lon keys are supported for maximum flexibility.

Content

MCP Weather Server

An MCP (Model Context Protocol) server providing USA weather information using the National Weather Service API. This server allows Claude and other MCP-compatible AI assistants to access real-time weather data.

Features

  • Weather Alerts : Get current weather alerts for any US state
  • Weather Forecasts : Get detailed weather forecasts for any location in the US using latitude and longitude

Installation

For Users

To use this MCP server with Claude or other MCP-compatible AI assistants:

# Install from PyPI
pip install mcp-weather

# Or install with uv
uv pip install mcp-weather

For Developers

To set up the development environment:

# Clone the repository
git clone https://github.com/michael7736/mcp-weather.git
cd mcp-weather

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -e ".[dev]"

Configuration

Cline (VS Code Extension)

Add the following to your Cline MCP settings file (cline_mcp_settings.json):

{
  "mcpServers": {
    "weather": {
      "command": "mcp-weather",
      "disabled": false,
      "autoApprove": []
    }
  }
}

Claude Desktop App

Add the following to your Claude Desktop config file (claude_desktop_config.json):

{
  "mcpServers": {
    "weather": {
      "command": "mcp-weather",
      "disabled": false,
      "autoApprove": []
    }
  }
}

Usage

Once configured, you can ask Claude or other MCP-compatible AI assistants questions about the weather:

  • "What are the current weather alerts in CA?"
  • "What's the weather forecast for San Francisco?" (The assistant will use coordinates for San Francisco)

Publishing

To publish this package to PyPI:

  1. Make sure you have the latest build tools:

    pip install --upgrade build twine

  2. Build the package:

    python -m build

  3. Upload to PyPI:

    python -m twine upload dist/*

License

This project is licensed under the MIT License - see the LICENSE file for details.

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