Weather CLI App MCP Server: Fetching Real-Time Weather Data for AI Applications

Overview: What is Weather CLI App MCP Server?

The Weather CLI App is an essential tool that leverages Model Context Protocol (MCP) to fetch real-time weather alerts and forecasts from the National Weather Service API. This server acts as a bridge between AI applications and critical data sources, enabling seamless integration of weather information into various workflows and decision-making processes.

🔧 Core Features & MCP Capabilities

At its core, the Weather CLI App offers two primary functionalities:

1. Real-time weather alert retrieval for any US state using two-letter postal codes.
2. Detailed weather forecasts for specific locations based on geographic coordinates (latitude, longitude).

These features are implemented through robust MCP protocols that ensure compatibility and interoperability with various AI applications, making it a powerful addition to the MCP ecosystem.

⚙️ MCP Architecture & Protocol Implementation

The Weather CLI App utilizes MCP to structure data and communication between the server and client. This architecture allows for a standardized interaction model where:

• MCP Clients (such as Claude Desktop, Continue, Cursor) can interact with the server using predefined commands.
• Data retrieval is encapsulated in structured MCP calls (get_alerts for alerts and get_forecast for forecasts).
• The data returned from the National Weather Service API is validated and formatted according to MCP standards.

MCP Protocol Implementation Details

The implementation involves:

1. Command Handling: The server listens for specific commands (e.g., get_alerts, get_forecast) which are structured in a way that can be understood by MCP clients.
2. Data Transformation: Raw API responses from the NWS are parsed and transformed into a format compatible with MCP, ensuring data is presented in an accessible manner to client applications.

Mermaid Diagram: MCP Protocol Flow

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

🚀 Getting Started with Installation

To set up the Weather CLI App as an MCP server, follow these installation steps:

1. Ensure you have Python 3.11 or higher installed.
2. Clone this repository:

   git clone https://github.com/username/weather-cli-app.git
   

3. Install the package using pip:

   # Install with pip
   pip install -e .
   
   # Or if you have pip3 specifically
   pip3 install -e .
   

💡 Key Use Cases in AI Workflows

The Weather CLI App enhances AI workflows by providing real-time weather data, crucial for a wide range of applications such as smart city management, disaster response planning, and environmental monitoring. For example:

• Smart City Management: Real-time weather alerts can trigger automated responses to manage traffic flow, public services, and resource allocation.
• Disaster Response Planning: Accurate weather forecasts enable early warning systems and better preparedness for potential disasters.

Mermaid Diagram: Data Architecture

graph TD
    A[Data Source (NWS API)]--Request-->B[MCP Server]
    B(MCP Server)--Data Transformation-->C[Structured Data]
    C(Structured Data)--Response-->D[MCP Client (AI Application)]

🔌 Integration with MCP Clients

The Weather CLI App is compatible with a variety of MCP clients, including:

• Claude Desktop: Full support for commands and data retrieval.
• Continue: Fully integrated with weather alert and forecast functionalities.
• Cursor: Capable of using the server for weather-related prompts but lacks full command execution.

Compatibility Matrix

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

📊 Performance & Compatibility Matrix

The server is designed to handle multiple concurrent requests and maintain API response times within normal operating limits. It supports various Python environments, ensuring compatibility across different deployment scenarios.

MCP Configuration Sample

To configure the Weather CLI App for use with your MCP client, you can include it in the configuration file as follows:

{
  "mcpServers": {
    "weather-server": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/weather-cli-app"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

🛠️ Advanced Configuration & Security

For advanced configuration, you can customize the server by updating environment variables and command-line arguments. Security is maintained through proper authentication mechanisms to protect API calls.

❓ Frequently Asked Questions (FAQ)

1. Can I use my own API key with this server? Yes, you can specify your API key in the configuration file under env settings.
2. How do I handle rate limits from the NWS API? The server automatically handles rate limits to ensure smooth operation without excessive interruptions.
3. Which MCP clients are supported by default? The server is pre-configured for compatibility with Claude Desktop and Continue, while Cursor can use tools but not command execution.
4. Is there a limit to the number of concurrent requests I can make? The server is designed to handle multiple concurrent requests efficiently within limits defined by NWS API rate policies.
5. How do I debug issues if the server fails during operation? You can run the server manually with verbose output using: python -m weather.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! For more information on developing and contributing to this project, see CONTRIBUTING.md.

MCP Ecosystem & Resources

The Weather CLI App is part of a broader ecosystem of MCP servers designed to integrate various data sources into AI workflows. Explore additional resources and servers at the MCP documentation site.

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By integrating the Weather CLI App with your AI applications, you can leverage real-time weather data to enhance decision-making processes across multiple domains.