MCP Weather Server: Universal Adapter for Weather Data Integration

Overview: What is MCP Weather Server?

MCP Weather Server is a specialized Model Context Protocol (MCP) server designed to provide real-time weather data via an Express API, seamlessly integrating with various AI applications through the standardized Model Context Protocol. This server serves as a bridge between AI tools and external weather APIs, enabling developers to leverage accurate and up-to-date weather information in their applications.

🔧 Core Features & MCP Capabilities

MCP Weather Server enhances AI application capabilities by offering robust weather data access through MCP. Key features include:

• Express API: Delivers weather information in a user-friendly format.
• OpenWeatherMap API Integration: Uses OpenWeatherMap for current and historical weather data.
• TypeScript Support: Ensures type safety and maintainability with TypeScript compilation.

This server supports multiple MCP clients, ensuring compatibility across different AI ecosystems. The following diagram illustrates the flow of an MCP protocol interaction:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[OpenWeatherMap API]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

⚙️ MCP Architecture & Protocol Implementation

The architecture of MCP Weather Server is built around the Model Context Protocol, ensuring seamless integration with various AI clients. Key implementation details include:

• MCP Client Compatibility: Full support for Claude Desktop, Continue, and Cursor.
• TypeScript Compilation: Ensures type safety during runtime.

Real-World Use Case: Weather Forecast Integration

Imagine a scenario where an AI assistant needs to provide users with weather forecasts based on their location. With MCP Weather Server, this integration can be effortlessly achieved:

1. AI Application Sends Request:

   • The AI application connects via MCP protocol, sending a request for the current weather in a specific city.

2. MCP Server Processes Request:

   • The server queries OpenWeatherMap using the provided API key.
   • The queried data is returned to the MCP client as structured JSON.

3. User Receives Response:

   • The user receives relevant weather information, enhancing their AI experience with accurate and timely data.

Similar use cases exist for various applications such as smart home systems or travel planning tools where real-time weather updates are crucial.

🚀 Getting Started with Installation

Installing the MCP Weather Server is straightforward. Follow these steps to get it up and running:

1. Clone the Repository:

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

2. Install Dependencies:

   npm install
   

3. Build TypeScript Sources:

   npm run build
   

4. Integrate with VS Code (Optional):

   • Add the MCP server configuration in launch.json as follows:

     "weather-mcp": 
     {
         "type": "stdio",
         "command": "node",
         "args": [
             "C:\\<PATH_TO_YOUR_REPO>\\mcp-weather-server\\dist\\index.js"
         ]
     }
     

💡 Key Use Cases in AI Workflows

This MCP server enhances existing AI workflows by providing weather data, which can be integrated into various applications. Here are two practical use cases:

Use Case 1: Smart Home Environment

Imagine a smart home system that needs to adjust its climate settings based on the current outdoor temperature and humidity levels.

1. AI Application Sends Request:

   • The AI assistant sends a request for weather data covering multiple cities.

2. MCP Server Fetches Data:

   • The server fetches real-time weather information from OpenWeatherMap using API keys.

3. Data Processed & Sent Back:

   • The processed weather data is sent back to the AI application, which then adjusts the heating and cooling systems accordingly.

Use Case 2: Travel Planning Tool

A travel planning tool that needs to provide users with weather updates for their destination city before booking flights or hotels.

1. Request Weather Data:

   • The user requests current weather conditions in a specific location.

2. Retrieve & Display Info:

   • The MCP server queries the API, retrieving and displaying relevant weather information on the travel planning tool’s interface.

3. User Receives Information:

   • Users receive real-time updates to plan their trips efficiently.

🔌 Integration with MCP Clients

MCP Weather Server supports integration with multiple AI clients, including:

• Claude Desktop: Full support for fetching and processing weather data.
• Continue: Supports seamless data retrieval and usage in conversational applications.
• Cursor: Limited support for data sources; primarily useful for tool integrations.

The following table outlines the current status of support for different MCP clients:

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

📊 Performance & Compatibility Matrix

The performance of MCP Weather Server is optimized for fast and reliable data retrieval. It ensures compatibility with multiple clients by adhering to the Model Context Protocol standards.

Mermaid Diagram: Performance & Compatibility Matrix

graph TD
    A[Performance] --> B[Fast Data Retrieval]
    C[Compatibility] --> D[MCP Clients]
    E[Fully Compatible]
    B --> E
    D --> E
    style B fill:#b5e4a2
    style C fill:#d3d9bb
    style E fill:#66ccff

🛠️ Advanced Configuration & Security

Configuring MCP Weather Server involves setting up environment variables and ensuring secure API key management.

Config Code Sample:

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

Environmental Variables:

export API_KEY=YOUR_API_KEY_FROM_OPENWEATHERMAP

❓ Frequently Asked Questions (FAQ)

Address common challenges and questions related to MCP weather server integration:

1. How do I integrate this with my AI application?

   • Ensure your AI application supports the Model Context Protocol and configure it according to the provided examples.

2. Can this server be used with any weather API?

   • Yes, the server is compatible with multiple weather APIs; however, OpenWeatherMap is currently supported for simplicity.

3. What are the performance considerations?

   • The server provides fast data retrieval using efficient queries and minimal latency.

4. How do I handle API key security?

   • Securely store your API keys in environment variables or a secure vault system to prevent unauthorized access.

5. Are there any limits on request frequency?

   • Refer to OpenWeatherMap’s API rate limits to ensure compliant usage of the service.

👨‍💻 Development & Contribution Guidelines

Contributing to MCP Weather Server involves setting up your development environment and understanding the project structure:

1. Set Up a Development Environment:

   git clone https://github.com/mohaimenhasan/mcp-weather-server.git
   cd mcp-weather-server
   npm install
   

2. Run Dev Server:

   npm run dev
   

3. File Issues and Pull Requests:

   • Use the GitHub issue tracker to report bugs or request new features.
   • Contribute code by creating pull requests, ensuring they follow best practices.

🌐 MCP Ecosystem & Resources

Explore more about the Model Context Protocol and its ecosystem:

• MCP Documentation: Find detailed documentation on the official website for a deeper understanding of the protocol.
• Community Support: Join developer communities where you can share experiences and learn from others working with MCP protocols.
• OpenWeatherMap API: Refer to their official documentation for more information on using this weather API.

This technical documentation positions MCP Weather Server as a valuable tool in integrating real-time weather data into various AI applications, ensuring compatibility, performance, and security.