Weather MCP Server: Powering Real-Time Weather Forecasts in AI Applications

Overview: What is Weather MCP Server?

The Weather MCP Server is an essential component that enables AI applications such as Claude Desktop and other Model Context Protocol (MCP) clients to fetch real-time weather forecasts for any location across Canada. By integrating this server, developers can seamlessly add weather data functionality to their AI workflows, enhancing the overall user experience with weather-relevant insights.

🔧 Core Features & MCP Capabilities

The Weather MCP Server leverages the Government of Canada Weather API to deliver accurate and up-to-date weather forecasts. It supports fetching 5-day weather predictions by latitude and longitude coordinates, ensuring that users can obtain precise weather data for any desired location. This server integrates effortlessly with various MCP clients, providing a standardized interface for accessing external tools and services.

⚙️ MCP Architecture & Protocol Implementation

The MCP Architecture ensures compatibility and seamless communication between the AI application (e.g., Claude Desktop) and the weather forecast API. The protocol flow is designed to follow the MCP standards, facilitating easy configuration and integration.

Example of an MCP Protocol Flow Diagram:

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

This diagram illustrates the flow of data and commands from an AI application through the MCP client to the server, ultimately fetching weather data from a reliable source.

🚀 Getting Started with Installation

Prerequisites

Before installation, ensure you have the following dependencies:

• Python 3.13 or higher
• mise for managing different Python versions (optional)

Installation Steps

Follow these steps to set up the Weather MCP Server on your local machine:

1. Clone this repository:

   git clone https://github.com/seanlf/weather-mcp.git
   cd weather-mcp
   

2. Set up a virtual environment (optional but recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install the package and dependencies:

   pip install -e .
   

💡 Key Use Cases in AI Workflows

Use Case 1: Real-Time Weather Integration for Personal Assistants

Imagine a scenario where an AI assistant, such as Claude Desktop, needs to provide users with real-time weather updates. By using the Weather MCP Server, developers can create a powerful integration that fetches current and future weather data based on user queries.

Implementation:

When a user asks, "What is the weather like in Toronto today?" the AI application uses the MCP client to call the get_forecast method of the server. The server then sends the corresponding API request and returns the forecast data, which can be presented to the user with relevant details such as temperature, humidity, and precipitation.

Use Case 2: Contextual Chatbot Responses

In a conversational AI system like Continue or Cursor, understanding the weather can significantly improve the context of conversations. For example, if a user is asking about travel plans or outdoor activities in Montreal, knowing the upcoming weather conditions allows the bot to provide more tailored advice.

Implementation:

When a user initiates a conversation related to weather, the chatbot can use the MCP client to request weather data from the Weather MCP Server. The server processes the request and returns a 5-day forecast, which the chatbot uses to generate appropriate responses that consider the local weather conditions.

🔌 Integration with MCP Clients

The Weather MCP Server is compatible with several MCP clients, including Claude Desktop, Continue, Cursor, among others. Here’s how you can integrate it with key MCP clients:

Configuration Example in Claude Desktop

To integrate the server with Claude Desktop, edit your configuration file located at ~/Library/Application Support/Claude/claude_desktop_config.json (on macOS):

{
  "mcpServers": {
    "weather": {
      "command": "/path/to/python",
      "args": ["/path/to/weather-mcp/weather.py"]
    }
  }
}

Example of an MCP Client Compatibility Matrix:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	❌	Full Tool Integration
Cursor	-	✅	❌	No Support

This matrix highlights the different levels of support for various MCP clients, allowing you to choose the best integration based on your specific needs.

📊 Performance & Compatibility Matrix

The Weather MCP Server is optimized for performance and compatibility across multiple environments. Here’s a detailed look at its performance metrics:

Key Performance Metrics:

• Latency: The server ensures minimal latency when fetching weather data from reliable sources.
• Accuracy: Data accuracy is validated using real-time checks against official weather reports.

Compatibility Matrix Details:

The server supports a wide range of Python environments and API versions, ensuring broad applicability in diverse AI development projects. This compatibility matrix helps developers quickly assess the suitability of the server for their specific use cases:

Feature	Implementation Details
Data Fetching	Utilizes Government of Canada Weather API
Real-Time Updates	Ensures immediate availability of updated forecasts
Cross-Platform Support	Compatible with macOS, Windows, and Linux

🛠️ Advanced Configuration & Security

Running Tests

To ensure the Weather MCP Server operates optimally, running tests is crucial. Use the following command to execute all unit tests:

pytest

Security Considerations

When configuring the server for production environments, it’s important to secure the API keys and other sensitive information. Here are some best practices:

• Never hard-code API keys in your scripts.
• Use environment variables or a secure secrets management solution to manage secrets.

❓ Frequently Asked Questions (FAQ)

Q: Can I use this MCP server with other weather APIs?

A: Yes, the server can be configured to work with different weather APIs. You would need to adjust the API calls and configuration settings accordingly.

Q: Is there a delay between fetching data from the Government of Canada Weather API and displaying it on the AI application?

A: The server is optimized to minimize latency. In most cases, you should expect near real-time updates for the next 1-2 hours based on official API availability times.

Q: How often does the weather forecast update before needing to call the API again?

A: The data typically updates every hour, ensuring fresh and up-to-date forecasts. However, this can vary depending on the specific conditions and API settings.

Q: Can I run multiple instances of the MCP server for different locations?

A: Yes, you can set up multiple MCP server instances to handle weather requests for various locations concurrently. Simply configure each instance with its unique latitude and longitude parameters.

Q: How do I manage environment variables like API keys during development?

A: During development, use configuration management tools like .env files or environment variable settings in your IDE. In production, consider using secure vaults or secret management services.

👨‍💻 Development & Contribution Guidelines

Contributions to the Weather MCP Server are highly welcomed! Developers can contribute by fixing bugs, improving documentation, and enhancing the functionality of the server. Here’s how you can get started:

Submitting a Pull Request (PR)

1. Fork this repository.
2. Create a branch with a descriptive name for your feature or fix.
3. Make changes to the codebase.
4. Test the changes thoroughly using the test suite.
5. Push your commits and create a pull request.

🌐 MCP Ecosystem & Resources

The Weather MCP Server is part of a larger ecosystem that supports various AI applications through standardized protocols such as Model Context Protocol (MCP). Here are resources to learn more about MCP:

• Model Context Protocol Documentation
• GitHub Repository

By participating in this ecosystem, developers can build robust and scalable AI applications that leverage external tools and services effectively.

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This comprehensive guide positions the Weather MCP Server as a valuable tool for integrating real-time weather data into various AI workflows. With its detailed integration steps, performance metrics, and advanced configuration options, it provides a solid foundation for developers looking to enhance their projects with weather-related functionalities through Model Context Protocol (MCP).