WeatherInfoMCP

An MCP (Model Context Protocol) server that provides access to environmental weather data for AI agents through the National Weather Service (NWS) API.

Features

• 🌍 Location Management: Create locations from addresses or coordinates
• 🌤️ Weather Observations: Fetch real-time weather data from NWS stations
• 📊 Data Extraction: Extract specific weather metrics (temperature, humidity, wind, descriptions)
• ⚠️ Weather Alerts: Get active weather alerts for locations
• 🌡️ HeatRisk Guidance: Access HeatRisk information and CDC dashboard links

Usage

Using Directly from GitHub (No Installation Required) ⚡

The easiest way to use WeatherInfoMCP is directly from GitHub without cloning or installing anything locally. This method uses uvx (from the uv package manager) to automatically download, install, and run the MCP server from the repository.

Prerequisites:

• uv must be installed (it provides the uvx command)

How it works:

• uvx automatically creates an isolated environment, installs the package from GitHub, and runs it
• No local repository clone needed
• No manual dependency management
• Always uses the latest version from the repository

With OpenAI Agents SDK

from agents import Agent
from agents.mcp import MCPServerStdio

# Configuration for direct GitHub usage
PARAMS_WEATHER_MCP = {
    "name": "weatherinfo-mcp",
    "command": "uvx",
    "args": ["--from", "git+https://github.com/Babakjfard/weatherinfo_mcp.git", "weatherinfo-mcp"]
}

weather_server = MCPServerStdio(params=PARAMS_WEATHER_MCP)
agent = Agent(
    name="weather-agent",
    model="gpt-4",
    mcp_servers=[weather_server]
)

With Claude Desktop

Add this to your Claude Desktop configuration file (typically ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "weatherinfo-mcp": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/Babakjfard/weatherinfo_mcp.git", "weatherinfo-mcp"]
    }
  }
}

Benefits of this approach:

• ✅ Zero setup - no cloning, no installation, no virtual environment management
• ✅ Always up-to-date - uses the latest version from GitHub
• ✅ Isolated - uvx manages dependencies in isolated environments
• ✅ Portable - works the same way on any system with uv installed

Note: The first run may take a moment as uvx downloads and sets up the package. Subsequent runs are faster due to caching.

As an MCP Server

The package can be used as an MCP server with any MCP-compatible client:

python -m weatherinfo_mcp.mcp_tools.main

Or using the installed script:

weatherinfo-mcp

With AI Agent Frameworks

OpenAI Agents SDK

If the package is installed in editable mode (pip install -e . or uv sync), you can use:

from agents import Agent
from agents.mcp import MCPServerStdio

params = {
    "name": "weatherinfo-mcp",
    "command": "python",
    "args": ["-m", "weatherinfo_mcp.mcp_tools.main"],
    # PYTHONPATH not needed if package is installed
}

weather_server = MCPServerStdio(params=params)
agent = Agent(
    name="weather-agent",
    model="gpt-4",
    mcp_servers=[weather_server]
)

If the package is not installed, specify the source directory:

import os

project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
params = {
    "name": "weatherinfo-mcp",
    "command": "python",
    "args": ["-m", "weatherinfo_mcp.mcp_tools.main"],
    "env": {
        "PYTHONPATH": os.path.join(project_root, "src")
    }
}

Claude Desktop Configuration

If the package is installed:

{
  "mcpServers": {
    "weatherinfo-mcp": {
      "command": "python",
      "args": ["-m", "weatherinfo_mcp.mcp_tools.main"]
    }
  }
}

If the package is not installed, use the full path to the virtual environment Python and set PYTHONPATH:

{
  "mcpServers": {
    "weatherinfo-mcp": {
      "command": "/full/path/to/project/.venv/bin/python",
      "args": ["-m", "weatherinfo_mcp.mcp_tools.main"],
      "env": {
        "PYTHONPATH": "/full/path/to/weatherinfo_mcp/src"
      }
    }
  }
}

Note: Using the virtual environment Python (/full/path/to/project/.venv/bin/python) ensures all dependencies are available.

Available Tools

The MCP server provides the following tools:

1. create_location - Create a location object from an address or coordinates
2. get_current_observation - Fetch current weather observation data
3. get_temperature_from_observation - Extract temperature value (Celsius)
4. get_humidity_from_observation - Extract relative humidity (%)
5. get_weather_description_from_observation - Extract weather description
6. get_wind_info_from_observation - Extract wind speed and direction
7. get_alerts - Get active weather alerts for a location
8. get_HeatRisk - Get HeatRisk guidance and CDC dashboard information

Prerequisites

• Python >= 3.12
• uv package manager (recommended) or pip
• Internet connection (for NWS API access)

Installation

Using uv (Recommended)

1. Clone the repository:

   git clone <repository-url> weatherinfo_mcp
   cd weatherinfo_mcp
   

2. Install dependencies and create virtual environment:

   uv sync
   

   This will:

   • Create a virtual environment in .venv/
   • Install all required dependencies
   • Install the package in editable mode

3. Activate the virtual environment (optional, but recommended):

   source .venv/bin/activate
   

4. Install notebook dependencies (optional, for running example notebooks):

   uv pip install -r notebooks/requirements.txt
   

5. Set up Jupyter kernel (for Jupyter notebooks):

   # Register the kernel with Jupyter
   .venv/bin/python -m ipykernel install --user --name=weatherinfo_mcp --display-name="Python (weatherinfo_mcp)"
   

   After this, you'll see "Python (weatherinfo_mcp)" in your Jupyter notebook kernel selector.

Using pip

1. Clone and navigate to the project:

   git clone <repository-url> weatherinfo_mcp
   cd weatherinfo_mcp
   

2. Create and activate a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install the package:

   pip install -e .
   

Running Tests

The project includes a comprehensive test suite to verify MCP protocol compliance and functionality. Once available, use the following instructions:

Prerequisites for Testing

Install test framework dependencies:

cd tests
make install

This will automatically use uv if available, or fall back to pip.

Alternatively, install manually:

# Using uv (if available)
uv pip install -r requirements-framework.txt

# Or using pip
pip install -r requirements-framework.txt

Running All Tests

cd tests
make test-all

This will run:

• Base MCP protocol conformance tests
• WeatherInfoMCP-specific functionality tests
• MCP Inspector integration tests

Running Specific Test Suites

# Base MCP protocol tests only
make test-base

# WeatherInfoMCP-specific tests only
make test-weatherinfo_mcp

# Session lifecycle tests
make test-lifecycle

# Error handling tests
make test-errors

# Simple stdio test
make test-stdio

# Verbose output
make test-verbose

Test Reports

Test reports are saved to tests/ directory:

• comprehensive_conformance_report.txt - Full test results
• base_conformance_report.txt - Base protocol tests
• weatherinfo_mcp_conformance_report.txt - Package-specific tests
• session_lifecycle_report.txt - Session management tests
• error_handling_report.txt - Error handling tests

Configuration

The package uses the NWS API which is free and requires no API key. The service automatically handles:

• Location geocoding using geopy
• Finding nearest NWS weather stations
• Fetching real-time observations

Development

Project Structure

weatherinfo_mcp/
├── src/
│   └── weatherinfo_mcp/
│       ├── __init__.py
│       ├── core/
│       │   └── nws_location_service.py  # Location service
│       └── mcp_tools/
│           ├── main.py                  # MCP server entry point
│           └── nws_weather_tools.py     # MCP tool definitions
├── tests/                                # Test suite
│   ├── Makefile                         # Test runner
│   ├── comprehensive_stdio_tests.py     # Comprehensive tests
│   ├── test_nws_location_service.py     # Unit tests
│   ├── test_nws_weather_tools.py        # Unit tests
│   └── ...
├── notebooks/                           # Example notebooks
├── pyproject.toml                       # Package configuration
├── uv.lock                              # Dependency lock file
└── README.md                            # This file

Setting Up Development Environment

1. Follow the installation steps above
2. Install additional development tools as needed:

   # Install common development tools (optional)
   uv pip install pytest black flake8 mypy
   # Or
   pip install pytest black flake8 mypy
   

Running Individual Unit Tests

# Using pytest
pytest tests/test_nws_location_service.py -v
pytest tests/test_nws_weather_tools.py -v

# Or using Python directly
python -m pytest tests/

Code Quality

# Format code (if black is installed)
black src/

# Lint code (if flake8 is installed)
flake8 src/

Troubleshooting

Virtual Environment Issues

If tests fail with "python: No such file or directory":

• Ensure you've activated the virtual environment: source .venv/bin/activate
• Or the Makefile will automatically detect the venv if present

Module Import Errors

If you get import errors:

# Reinstall the package in editable mode
uv sync
# Or
pip install -e .

Test Failures

If tests fail due to NWS API errors:

• Check your internet connection
• The NWS API may occasionally be unavailable (tests should handle this gracefully)
• Some test failures may be due to temporary API outages, not code issues

License

MIT License - See LICENSE file for details.

Author

Babak J.Fard - babak.jfard@gmail.com

References

• Model Context Protocol Specification
• National Weather Service API
• FastMCP Documentation