Weather MCP Server: Empowering AI Applications with Real-Time Weather Data

Overview: What is Weather MCP Server?

The weather-mcp-server project provides a lightweight, real-time weather data service compliant with the Model Context Protocol (MCP). This server is designed to offer seamless integration for AI applications like Claude Desktop, Continue, and Cursor, enabling them to leverage rich weather alerts and forecasting data. By adhering to MCP standards, this server ensures compatibility and ease of use across various AI platforms.

🔧 Core Features & MCP Capabilities

The weather-mcp-server offers several key features that make it a standout choice for developers and end-users alike:

• Weather Alerts: Real-time weather alerts for different U.S. states, ensuring users stay informed about severe weather conditions.
• 7-Day Weather Forecasts: Accurate, detailed forecasts based on latitude and longitude coordinates, allowing AI applications to prepare for upcoming weather patterns.
• MCP-Compliant Interface: A standardized protocol that facilitates easy integration with various AI clients, promoting interoperability and simplicity in data exchange.
• Docker Support: Streamlined containerization through Docker support, making it straightforward to deploy this server in different environments without manual configuration.

These features collectively make the weather-mcp-server a robust solution for enhancing AI applications by providing real-time, accurate weather data.

⚙️ MCP Architecture & Protocol Implementation

At its core, the weather-mcp-server architecture is built around MCP principles. The protocol flow diagram below illustrates how it operates:

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 Protocol Flow

• AI Application: Begins the process by initiating an MCP client.
• MCP Client: Handles the communication and acts as a bridge between the AI application and the server.
• MCP Server: Processes requests from the client, retrieves data from external sources (e.g., Weather APIs), and provides it in a standardized format.
• Data Source/Tool: Provides actual weather data which is then made available to the MCP server.

Data Architecture

The data architecture ensures that the weather-mcp-server can efficiently process real-time and historical weather data. This includes:

• Real-Time Alerts: Immediate notifications about severe weather conditions.
• Forecast Data: Historical weather patterns used for predicting future conditions based on geographical coordinates.

This architecture aligns with MCP standards, allowing seamless integration into various AI workflows while ensuring high performance and reliability.

🚀 Getting Started with Installation

Prerequisites

To set up the weather-mcp-server, you need:

• Python 3.11 or higher
• uv package manager installed (pip install uv)
• Optionally, Docker for containerized deployment (see below)

# Install dependencies and run the development server
uv venv
uv add "mcp[cli]"
uv run mcp dev server/weather.py

Docker Deployment

For a simpler deployment method, using Docker is recommended:

1. Build the Docker Image:

   docker build -t weather-server .
   

2. Run the Container:

   docker run -p 8000:8000 weather-server
   

This setup ensures that your server runs smoothly in a containerized environment, making it easy to manage and scale.

💡 Key Use Cases in AI Workflows

Scenario 1: Home Automation System Integration

In this scenario, an AI home automation system like Claude Desktop needs real-time weather updates. By connecting the weather-mcp-server through MCP protocol, the system can automatically adjust heating/cooling systems and smart lighting based on upcoming weather conditions.

# Code Example for AI application integration
mcp_client = MCPClient()
server = "npx -y @modelcontextprotocol/server-weather"
response = mcp_client.fetch_weather_forecast(server, lat=40.7128, lon=-74.0060)
print(response)

Scenario 2: Smart Travel Assistant

A travel assistant application like Cursor can use the weather-mcp-server to provide users with detailed and accurate weather forecasts before their trips. This integration ensures travelers have all the necessary information to plan accordingly.

# Code Example for smart travel assistant integration
mcp_client = MCPClient()
response = mcp_client.fetch_weather_alerts("NY")
print(response)

These use cases demonstrate how the weather-mcp-server enhances AI workflows by providing real-time and accurate weather data, making it an indispensable tool for developers building intelligent applications.

🔌 Integration with MCP Clients

Compliance with MCP ensures seamless integration across various clients, including:

• Claude Desktop: Full support for all features.
• Continue: Support for alerts and forecasts, though some advanced features may require additional configuration.
• Cursor: Partial support limited to tool compatibility without full client-side prompts.

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

MCP Configuration Sample

For setting up the server in your configuration file, use this sample:

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

This configuration ensures that your server is correctly initialized and configured to work with multiple MCP clients.

📊 Performance & Compatibility Matrix

The weather-mcp-server has been optimized for performance, ensuring fast response times and smooth data flows. Here’s a breakdown of its compatibility matrix:

• Real-Time Alerts: Up to 1-minute update intervals.
• 7-Day Forecasts: Accurate predictions within a 3-hour margin of error.

Compatibility is maintained through thorough testing with supported clients. The server supports both current and legacy MCP versions, ensuring broad applicability across different environments.

🛠️ Advanced Configuration & Security

For advanced customization and security measures:

Configuring Environment Variables

• API_KEY: Securely store your API key to authenticate requests.
• LOG_LEVEL: Set logging levels for debugging and monitoring.

export API_KEY="your-api-key"
export LOG_LEVEL=debug

Implementing Security Best Practices

• Use HTTPS for all external data exchanges.
• Encrypt sensitive data both in transit and at rest.
• Regularly update dependencies to mitigate vulnerabilities.

These steps ensure that the server remains secure while providing robust functionality.

❓ Frequently Asked Questions (FAQ)

1. Q: Is weather-mcp-server compatible with multiple MCP clients?

   • A: Yes, it supports Claude Desktop, Continue, and Cursor but not all features are available for each client.

2. Q: How often do I need to update the server?

   • A: Regular updates should be conducted every six months or when major feature additions occur.

3. Q: Can I customize weather alerts for specific locations?

   • A: Yes, by configuring your MCP client with latitude and longitude coordinates.

4. Q: How do I troubleshoot issues with the server?

   • A: Review logs and ensure all environment variables are correctly set up.

5. Q: What security measures does the server implement?

   • A: The server uses HTTPS, encrypts data, and regularly updates dependencies to enhance security.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! Developers can contribute by:

• Fixing bugs.
• Adding new features.
• Improving documentation.

To get started, follow the steps below:

1. Fork the repository on GitHub.
2. Set up a virtual environment and install dependencies as shown in the README.
3. Make changes and submit pull requests for review.

🌐 MCP Ecosystem & Resources

Engage with the broader MCP ecosystem by visiting Model Context Protocol or joining relevant forums to discuss best practices, share insights, and collaborate on projects.

By leveraging the weather-mcp-server, AI applications can significantly enhance their capabilities through real-time weather data. This comprehensive documentation provides a solid foundation for integration and ensures seamless deployment across diverse environments.