Weather App MCP Server: Command-Line and Model Context Protocol-Based Weather Application

Overview: What is Weather App MCP Server?

The Weather App MCP Server is an advanced, command-line-based weather application designed to provide real-time weather conditions and forecasts for locations worldwide. It leverages the Model Context Protocol (MCP) to seamlessly integrate with AI applications such as Claude Desktop, Continue, Cursor, and others, allowing them to leverage a standardized, reliable interface for accessing weather data.

🔧 Core Features & MCP Capabilities

Weather Information

Current Weather Conditions: Retrieve real-time temperature, humidity, pressure, and more.
Daily Forecasts: Get detailed daily forecasts including high and low temperatures, precipitation probabilities, and wind speeds.
Hourly Forecasts: Observe hourly weather conditions to plan short-term activities.
Weather Alerts: Stay informed about severe weather alerts such as thunderstorms, hurricanes, and heatwaves.

Location-Based Services

Automatic Location Detection: Automatically determine the user's location if provided permission, or manually set coordinates.
Test API Connection: Quickly verify that your API key is correctly configured and active.
Check API Key and Subscription Status: Ensure seamless access to OpenWeatherMap's services without interruptions.

Command-line Interface

The server exposes a versatile command-line interface with multiple tools:
- get_current_weather: Retrieve current weather conditions.
- get_forecast: Obtain daily forecast details.
- get_hourly_forecast: View hourly conditions.
- get_alerts: Stay updated on severe weather events.
- get_weather_by_coordinates: Fetch weather data by geographic coordinates.
- get_user_location: Determine current location or set a new one.
- test_api_connection: Test the API to ensure connectivity.
- check_api_key_and_subscription: Validate your API key and subscription status.

MCP Protocol Implementation

The Weather App MCP Server follows the Model Context Protocol (MCP) for seamless integration with AI applications. The protocol ensures that data requests are standardized, making it easier for AI applications to consume and utilize weather-related services. The server implements a robust MCP endpoint that supports various API calls:

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

MCP Client Compatibility Matrix

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

This matrix highlights that while all clients support data resources and tools, not all support prompts, indicating that full compatibility is available for tools.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the Weather App MCP Server ensures reliable and efficient integration with AI applications. It uses Python 3.12 or higher and relies on the uv package manager for dependencies, streamlining setup and configuration. The server runs a FastMCP framework that exposes API endpoints following MCP protocol standards.

🚀 Getting Started with Installation

To get started with installing the Weather App MCP Server, you'll need to meet certain prerequisites:

Prerequisites

Python 3.12 or higher
uv package manager

Setup Steps

Clone the repository:

git clone <repository-url>
cd weather-app

Create a .env file and provide your OpenWeatherMap API key:
```
OPENWEATHER_API_KEY=your_api_key_here
```
Run the installation script with or without development mode options:

For standard setup, use:
```
./scripts/install.sh
```
For a development environment, add --dev flag:
```
./scripts/install.sh --dev
```

💡 Key Use Cases in AI Workflows

Use Case 1: Real-time Weather Monitoring for Personal Assistants

Imagine integrating the Weather App MCP server into an AI-powered personal assistant that provides users with real-time weather updates. When a user asks, "What's the current temperature outside?" the assistant can fetch the data from theWeather App MCP Server, ensuring accuracy and reliability.

Use Case 2: Smart Home Automation Based on Weather Conditions

AI-driven home automation systems can use the Weather App MCP server to react to different weather conditions. For example, if it's raining heavily, smart lights could be dimmed or smart shutters closed automatically. This integration would leverage hourly forecast data and alerts from the MCP server.

🔌 Integration with MCP Clients

Integration with MCP clients is designed to be straightforward, providing a seamless experience for users and developers. The server supports automatic detection of API credentials and location services, ensuring that AI applications can quickly begin accessing relevant weather information.

📊 Performance & Compatibility Matrix

The performance of the Weather App MCP Server is optimized for efficiency when handling multiple requests from various AI clients. It ensures minimal latency and smooth operation, making it ideal for real-time applications.

Sample Configuration Code

import os
from dotenv import load_dotenv
from mcp.server.fastmcp import FastMCP

# Load environment variables
load_dotenv()

# Initialize FastMCP server
mcp = FastMCP("WeatherApp")

# Define your tools
@mcp.tool
async def get_current_weather(location: str) -> str:
    """Fetch current weather conditions for a given location."""
    # Your implementation here
    return "Current Weather Data"

# Run the server
if __name__ == "__main__":
    mcp.run()

🛠️ Advanced Configuration & Security

For more advanced configurations and enhanced security, you can customize the environment variables and add additional tools as needed. The FastMCP framework allows for secure handling of API keys, ensuring data privacy and integrity during queries.

❓ Frequently Asked Questions (FAQ)

Q: Can this server be integrated with different MCP clients?
- Yes, the Weather App MCP Server supports integration with Claude Desktop, Continue, and Cursor through a comprehensive compatibility matrix that outlines specific feature support for each client.
Q: How can I test if my API key is working correctly?
- You can use the test_api_connection tool provided by the server to quickly verify your API key's validity without interrupting other functionalities.
Q: Can this server be run locally, or must it be hosted online?
- This server can be run both locally and remotely. For development purposes, it is recommended to start with a local setup for easier debugging and testing.
Q: How do I set up custom environment variables while running the server?
- You can manually configure .env files or specify them during installation using --env-override flags provided by the uv manager.
Q: Is there documentation available for advanced tool creation and configuration?
- Yes, a comprehensive documentation guide is included in this repository that explains how to create custom tools and methods compatible with MCP standards.

👨‍💻 Development & Contribution Guidelines

Contributions are highly encouraged from the developer community. If you wish to contribute, ensure you adhere to our development practices by setting up pre-commit hooks for automated code formatting and linting. Contributions can be made through pull requests, addressing issues, or adding new feature implementations.

🌐 MCP Ecosystem & Resources

For developers looking to integrate with various MCP servers and tools, the Weather App MCP Server serves as a robust foundation that aligns seamlessly with the broader MCP ecosystem. Explore other open-source projects within this ecosystem for additional inspiration and integration ideas.

By following these guidelines and leveraging the Weather App MCP Server, you can significantly enhance your AI applications by providing reliable, real-time weather information through a standardized protocol.

Weather App