Weather MCP Server: Enhancing AI Applications with MCP

Overview: What is Weather MCP Server?

The Weather MCP Server is a learning project designed to explore and implement the Model Context Protocol (MCP) for integrating external data sources into artificial intelligence applications. Specifically, this server utilizes the National Weather Service (NWS) API to provide weather alerts and forecasts, allowing AI assistants like Claude Desktop, Continue, Cursor, and others to access real-time and forecasted weather information through a standardized protocol.

🔧 Core Features & MCP Capabilities

The Weather MCP Server offers two key capabilities:

1. get_alerts: Retrieve active weather alerts for any US state.
2. get_forecast: Obtain weather forecasts based on specific location coordinates (latitude and longitude).

These features enable AI applications to provide contextually relevant and timely weather information, enhancing user experience and utility.

⚙️ MCP Architecture & Protocol Implementation

MCP is designed to facilitate the interaction between AI assistants and external tools or data sources. The Weather MCP Server uses Python with libraries such as httpx for HTTP requests and the mcp library to adhere to the protocol standards. This implementation ensures compatibility across various MCP clients, including Claude Desktop, Continue, and Cursor.

Mermaid Diagram: MCP Protocol Flow

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

This diagram illustrates the interaction between an AI application, the MCP Client, the Weather MCP Server, and external weather data sources.

🚀 Getting Started with Installation

To set up and run the Weather MCP Server:

1. Clone the Repository:

   git clone https://github.com/user/repo.git
   cd repo
   

2. Install Dependencies:

   pip install mcp httpx
   # or use uv:
   uv pip install mcp httpx
   

3. Configure MCP Server in Your AI Assistant:

In Claude Desktop

Add the following configuration to the Claude Desktop settings:

{
  "mcpServers": {
    "weather": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather",
        "run",
        "weather.py"
      ]
    }
  }
}

In Cursor

Go to Cursor Settings -> MCP Servers -> Add MCP Server:

• Name: Weather
• Type: Command
• Command: uv run /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/weather.py

💡 Key Use Cases in AI Workflows

1. Real-Time Weather Alerts:
   • Integrating the server with an emergency notification system to alert users about severe weather conditions.
2. Forecast-Based Decision Making:
   • Leveraging accurate forecasts for urban planning or event scheduling, ensuring optimal resource utilization.

🔌 Integration with MCP Clients

The Weather MCP Server is compatible with multiple MCP clients:

• Claude Desktop: Full support for data retrieval and integration.
• Continue: Supports both data ingestion and contextual interaction through prompts.
• Cursor: Compatible for interactive sessions but lacks direct tool access.

📊 Performance & Compatibility Matrix

MCP Client Compatibility Table:

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

This table outlines the current status of integration for different MCP clients, highlighting areas where additional development may be required.

🛠️ Advanced Configuration & Security

To secure and configure the Weather MCP Server:

1. Environment Variables: Set up environment variables to manage API keys securely.
2. Security Practices: Implement rate limiting and authentication mechanisms to prevent unauthorized access.

Mermaid Diagram: Data Architecture

graph TD
    A[Data Source] --> B[Weather Data API]
    B --> C[MCP Server]
    C --> D[AI Application Backend]

This diagram visualizes the data flow from external sources, through the Weather MCP Server, and into the AI application backend for processing.

❓ Frequently Asked Questions (FAQ)

1. Q: Which MCP clients are fully compatible with this server?
   • A: Both Claude Desktop and Continue offer full compatibility.
2. Q: How can I improve security in my implementation?
   • A: By using environment variables to manage API keys securely and implementing rate limiting.
3. Q: Can I use this server for other data sources besides weather alerts and forecasts?
   • A: Yes, the server design is modular; you can adapt it for various types of datasets with appropriate data handlers.
4. Q: Are there any limitations to using this in production environments?
   • A: Currently, only Cursor lacks direct tool access, making full compatibility a factor depending on your use case.
5. Q: How do I set up environment variables for secure API key management?
   • A: Use .env files or system-specific configuration methods like os.environ.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! If you wish to contribute, please follow these guidelines:

1. Fork the Repository: Fork this project and make your changes.
2. Branching: Create a new branch for your feature or bug fix.
3. Testing: Ensure all tests pass before submitting a pull request.

🌐 MCP Ecosystem & Resources

For more information on MCP and related resources, visit:

• Model Context Protocol Documentation
• MCP Community Forum

Join the community to stay updated on developments and participate in discussions about MCP and its applications.

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This comprehensive technical documentation highlights the Weather MCP Server's capabilities, integration with various AI clients, performance, security considerations, and real-world use cases. It positions this project as a valuable resource for developers looking to enhance their AI applications with MCP-compliant servers.