MCP Weather Alerts Server: Building AI-Driven Weather Insight Tools

Overview: What is the MCP Weather Alerts Server?

The MCP Weather Alerts Server utilizes the Model Context Protocol (MCP) to construct a specialized tool that serves real-time weather alerts for U.S. states. This server, which leverages the FastMCP Python SDK and integrates with Claude Desktop, enables AI applications to retrieve detailed meteorological notifications swiftly. By implementing FastMCP, this project streamlines the process of fetching up-to-date information from the api.weather.gov API and returning structured data tailored for practical use.

🔧 Core Features & MCP Capabilities

The core features of our MCP Weather Alerts Server encompass both the technical implementation details and how it integrates seamlessly with various AI clients. The server employs asynchronous HTTP requests via httpx, ensuring fast response times and efficient usage of resources. Structured information such as event, severity, description, and instructions are returned for each weather alert, making it easy for developers to process and implement within their applications.

MCP Capabilities:

1. Real-Time Weather Alerts: The server leverages the api.weather.gov API to fetch live weather alerts based on specified U.S. states.
2. Structured Data Response: Returned data includes key information such as event, severity level, detailed descriptions, and actionable instructions, facilitating easy integration with downstream tools or user interfaces.
3. MCP Compatibility: Our server is designed to work seamlessly with major AI clients like Claude Desktop, Continue, and Cursor, enhancing their ability to deliver contextually relevant insights.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the MCP Weather Alerts Server is built on a modular approach that adheres strictly to the MCP protocol. It integrates FastMCP for server functions and uses httpx for HTTP requests. The tools are managed using uv, a modern package manager in Python.

Technical Stack Configuration:

• mcp[cli]: MCP server toolkit with comprehensive CLI capabilities.
• uv: Modern dependency manager for the project.
• httpx: Asynchronous HTTP client library for efficient interactions.

The following diagram illustrates the flow of data and protocol interactions within our setup.

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

🚀 Getting Started with Installation

To begin using the MCP Weather Alerts Server, follow these steps to ensure a smooth setup process. This guide provides detailed instructions for installing the necessary tools and configuring your environment.

1. Create & Configure Project

Firstly, install uv if you haven't already:

pipx install uv

To initialize a new project using uv, execute the following command in your terminal:

uv init mcpcrashcourse
cd mcpcrashcourse

Next, add the required dependencies for our server tool logic with uv:

uv add "mcp[cli]" httpx

2. Add Your Tool

Place the Python script that implements your get_alerts function in a file under server/, e.g., weather.py. This module contains the server's primary logic for fetching and processing weather alerts.

from fastmcp import mcp, tool

@tool
async def get_alerts(state: str) -> list[dict]:
    # Fetch weather alerts for a given U.S. state (e.g., 'CA')
    url = "https://api.weather.gov/alerts/active"
    params = {"area": state.upper()}
    
    async with httpx.AsyncClient() as client:
        response = await client.get(url, params=params)
        data = response.json()
        
    return [
        {
            "event": alert.get("event"),
            "severity": alert.get("severity"),
            "description": alert.get("description"),
            "instruction": alert.get("instruction"),
        }
        for alert in data.get("features", [])
        if alert.get("properties")
    ]

💡 Key Use Cases in AI Workflows

The MCP Weather Alerts Server serves as a critical component for developers creating applications that require real-time weather updates. Here are two practical use cases:

Real-Time Emergency Preparedness

For disaster response teams, the server can provide timely warnings and detailed instructions about ongoing weather events. This information is vital for formulating immediate responses or evacuation plans.

Smart Home Integration

In smart home systems, the alerts can trigger automated actions such as turning off outdoor lights during power outages due to storms or adjusting thermostat settings based on extreme temperatures.

🔌 Integration with MCP Clients

To integrate this server with an AI client like Claude Desktop, follow these steps:

1. Update claude_desktop_config.json:

{
  "mcpServers": {
    "weather": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "<your-project-directory>/server/weather.py"
      ]
    }
  }
}

2. Restart Claude Desktop: Ensure the application picks up the new configuration and starts using the MCP server for weather data.

3. Invoke Tool:

Prompt your AI client with:

What are the weather alerts for Texas?

📊 Performance & Compatibility Matrix

The compatibility of our Weather Alerts Server is paramount, ensuring seamless interactions across various AI clients. The following table details the current status and compatibility level of each client.

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	❌	✅	✅	Full Support
Continue	✔️	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🛠️ Advanced Configuration & Security

Security measures are crucial for protecting against unauthorized access and data breaches. Here's how you can secure your setup:

• API Key Management: Ensure that sensitive API keys are stored securely, preferably using environment variables.
• Rate Limiting: Implement rate limiting to prevent abuse of the weather API.

For advanced users who wish to customize their experience further:

API_KEY="your-secure-api-key"
uv run mcp dev server/weather.py --env-file .env

❓ Frequently Asked Questions (FAQ)

Q1: How does this MCP Weather Alerts Server integrate with Claude Desktop?

A: The server integrates by defining an MCP endpoint that can be invoked through claude_desktop_config.json. This setup ensures Claude Desktop can seamlessly access real-time weather alerts.

Q2: Can I use this server with other AI applications besides Claude Desktop and Continue?

A: While our focus is on compatibility with Claude Desktop and Continue, the codebase and documentation can serve as a foundation for similar integrations with other clients that support MCP.

Q3: How do I handle rate limiting in weather.py to prevent API overuse?

A: Implement rate limit checks within your server logic. For instance:

import time

RATE_LIMIT = 10  # API calls per minute
def enforce_rate_limit():
    global last_call_time
    current_time = int(time.time())
    time_since_last_call = current_time - last_call_time
    if time_since_last_call < RATE_LIMIT:
        sleep_time = (time_since_last_call + 60 / RATE_LIMIT) - current_time
        time.sleep(sleep_time)
    else:
        last_call_time = current_time

Q4: What are the system requirements to run this MCP Weather Alerts Server?

A: The server runs on any machine compatible with Python and uv. Basic configuration is straightforward, but optimizing performance may require a higher-spec machine due to asynchronous operations.

Q5: How do I debug or test the server interactively?

A: Use the MCP Inspector tool provided by FastMCP. Running:

uv run mcp dev server/weather.py

Opens an interactive interface where you can test your tools in real-time, ensuring they function correctly before deployment.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! To contribute to the project, ensure your code adheres to best practices and follow these steps:

1. Fork the Repository: Go to GitHub and fork the repository.
2. Clone Your Fork: Clone the repository to your local machine.

git clone https://github.com/your-username/mcpcrashcourse.git

3. Develop & Test: Make necessary changes, test thoroughly, and commit them with clear messages.

4. Submit PR: After testing locally, submit a pull request detailing the changes made.

🌐 MCP Ecosystem & Resources

Explore additional resources within the Model Context Protocol ecosystem:

• Visit the official documentation for comprehensive guidance.
• Join relevant communities and forums where you can discuss MCP implementation details and best practices with fellow developers.

Conclusion

By leveraging the MCP Weather Alerts Server, developers can build robust applications that provide real-time weather alerts using a standardized protocol. This server enhances AI integrations, ensuring reliable data delivery across various platforms while catering to specific user needs through structured output.