Weather MCP Server Powered by the Open-Meteo API

The Weather MCP Server is an essential tool that integrates seamlessly with various AI applications, leveraging the powerful Open-Meteo API to provide detailed weather information and forecasts. This server acts as a bridge, enabling AI clients such as Claude Desktop, Continue, Cursor, and more to access real-time weather data through the Model Context Protocol (MCP).

Overview: What is Weather MCP Server?

The Weather MCP Server is designed to enhance AI applications by offering a standardized interface for integrating with diverse weather-related tools. By supporting MCP, this server simplifies the process of fetching current weather conditions and forecasts, making it indispensable for any application that needs accurate and up-to-date climate data.

🔧 Core Features & MCP Capabilities

This MCP server boasts several robust features:

• Real-Time Weather Data: Fetches live weather information for specified locations.
• Weather Forecasts: Provides detailed predictions for future times and dates.
• User Queries: Supports natural language processing (NLP) queries, such as "Will it rain tomorrow?" or "Do I need sunscreen today?"

MCP Protocol Implementation

The Weather MCP Server adheres to the Model Context Protocol (MCP), a universal framework that ensures compatibility with a wide range of AI applications. This protocol defines how data is exchanged between the server and client applications, ensuring seamless integration.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the Weather MCP Server follows a structured design:

1. Client Initialization: The AI application initializes an MCP Client that connects to the Weather MCP Server.
2. Command Execution: The MCP Client sends commands and requests using the MCP protocol, which are then processed by the server.
3. Data Fetching & Processing: The server interacts with the Open-Meteo API to fetch weather data and processes it according to the requested format.

Mermaid Diagram: MCP Protocol Flow

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

Mermaid Diagram: Data Architecture

graph LR
    A[MCP Client] -->|Query| B[Weather MCP Server]
    B --> C[Open-Meteo API]
    C -->|Data| D[Weather Database]
    D --> E[Result Formatting]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

🚀 Getting Started with Installation

Installing the Weather MCP Server is straightforward and involves just a few steps:

1. Dependencies: Ensure you have the required dependencies installed:

   brew install uv  # macOS via Homebrew
   winget install --id=astral-sh.uv -e  # Windows via WinGet
   

2. Environment Setup: Activate the virtual environment and run the server using MCP:

   source .venv/bin/activate
   mcp dev server.py
   

💡 Key Use Cases in AI Workflows

The Weather MCP Server is particularly useful in scenarios where real-time weather data is crucial:

• Home Management Systems: Automate home thermostats based on forecasted temperatures.
• Travel Planning: Provide travelers with up-to-date weather conditions before embarking on a journey.

Real-World AI Workflow Scenario 1: Smart Home Automation

In a smart home setting, the Weather MCP Server can integrate with other devices to adjust heating or cooling systems. For instance, when the forecast predicts a sudden drop in temperature, the server can notify the thermostat to increase warmth, ensuring comfort without manual intervention.

Real-World AI Workflow Scenario 2: Travel Planning Tool

A travel planning assistant could use this server to provide travelers with detailed weather forecasts. By integrating the API calls into a user interface, users can make informed decisions about packing, activities, and travel plans based on expected weather conditions.

🔌 Integration with MCP Clients

Compatibilities with various AI applications:

• Claude Desktop: Full support for fetching real-time data.
• Continue: Supports structured prompts and commands.
• Cursor: Limited to tool access without full command capabilities.

Mermaid Diagram: MCP Client Compatibility Matrix

graph LR
    A[Claude Desktop] --> B[✅]
    B --> C[Resources, Tools, Prompts]
    D[Continue] --> E[✅]
    F[Cursor] --> G[❌]

📊 Performance & Compatibility Matrix

The performance and compatibility of the Weather MCP Server are optimized to handle a wide range of queries and data requests efficiently.

• Latency: <1 second for most API calls.
• Data Response Time: Sub-second response times when fetching local weather conditions.

Real-World AI Workflow Implementation Sample

Suppose we’re building an AI scheduler for a travel planner. The server would be configured to provide weather updates every 24 hours and forecasted weather details every two days, ensuring the user is always informed about upcoming changes in the climate.

🛠️ Advanced Configuration & Security

For advanced users, the following configuration options are available:

• API Key Management: Securely store API keys using environment variables.
• Custom Commands: Define custom commands and prompts for fine-tuned data requests.

Real-World AI Workflow Sample: Custom Command Implementation

To create a custom command to fetch daily weather conditions, add the following snippet to your configuration file:

{
  "mcpServers": {
    "weather": {
      "command": "/absolute/path/to/uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "/absolute/path/to/open-meteo-weather/server.py"
      ]
    }
  }
}

❓ Frequently Asked Questions (FAQ)

1. Q: How do I integrate this server with Continue?

   • A: Ensure your claude_desktop_config.json is updated with the correct command for Open-Meteo weather queries.

2. Q: Can I customize the output format of the API responses?

   • A: Yes, you can modify the configuration to tailor the response structure according to your needs.

3. Q: How often does the server refresh data?

   • A: Data is refreshed every 15 minutes by default but can be adjusted in the configuration settings.

4. Q: What if I encounter errors during installation?

   • A: Check the logs for detailed error messages and ensure all dependencies are correctly installed.

5. Q: Can this server handle multiple locations simultaneously?

   • A: Yes, it supports concurrent queries for different geographic regions.

👨‍💻 Development & Contribution Guidelines

Contributions to improve the Weather MCP Server are welcome. Please follow these guidelines:

• Fork the project on GitHub.
• Develop your changes and submit a pull request (PR) containing detailed documentation.

🌐 MCP Ecosystem & Resources

Explore more about Model Context Protocol (MCP) and its ecosystem at the official website: modelcontextprotocol.com

For further support, join the community forums or reach out to the maintainers directly.

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By leveraging the Weather MCP Server, developers can enhance their AI applications with reliable and up-to-date weather information, making it an essential component for integrating into a variety of smart systems and tools.