Weather MCP Server: Integrating Real-Time Weather Data into AI Applications

Overview: What is Weather MCP Server?

The Weather MCP Server is an MCP (Model Context Protocol)-enabled server that provides a standardized interface for AI applications to query and utilize weather-related data. By leveraging the Model Context Protocol, this server ensures seamless integration with a variety of AI tools such as Claude Desktop, Continue, Cursor, and more. It serves as a versatile tool for developers looking to incorporate real-time weather updates into their applications without writing complex custom integrations.

🔧 Core Features & MCP Capabilities

The Weather MCP Server enhances the functionality of AI applications by offering a wide array of weather-related tools through a standardized interface:

• Real-Time Weather Conditions: Provides up-to-date information on current weather conditions.
• Weather Forecasts: Offers 1-day to 2-week forecasts, enabling accurate planning and decision-making.
• Historical Weather Data: Accesses past weather records for trend analysis and research.
• Weather Alerts: Delivers real-time alerts about severe weather conditions, ensuring timely public safety measures.
• Air Quality Information: Tracks air quality metrics essential for health and environmental monitoring.
• Astronomy Data: Includes sunrise, sunset times, and moon phases to connect astronomical data with weather forecasts.

These capabilities are implemented using the MCP protocol, which acts as a bridge between AI applications and various data sources. The server supports multiple protocols and ensures compatibility across different client environments.

⚙️ MCP Architecture & Protocol Implementation

The Weather MCP Server is built on FastAPI, a modern asynchronous web framework for Python that complements its performance and ease of use. It adheres to the Model Context Protocol by implementing specific API endpoints and data structures tailored for weather-related queries.

The server architecture consists of several components:

• MCP Client: Connects AI applications to the MCP server, handling protocol translation.
• Data Sources/Tools: Integrate with external APIs and databases to fetch relevant weather information.
• API Endpoints: Define the interactions between MCP clients and the server through RESTful methods.

Mermaid: 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

Mermaid: Weather Data Architecture

graph TD
    subgraph WeatherServer
        A[Weather API] --> B[MCP Server]
        B --> C[MCP Client]
        C --> D[AI Application]
        style A fill:#e5f2ff
        style B fill:#ffe8e8
        style C fill:#f0f7e4
    end

🚀 Getting Started with Installation

To get the Weather MCP Server up and running, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/Weather_mcp_server.git
   cd Weather_mcp_server
   

2. Install dependencies using uv (a package manager):

   uv venv
   uv pip install -e .
   

3. Create a .env file in the project root and add your WeatherAPI key:

   WEATHER_API_KEY=your_api_key_here
   

4. Run the server to start it on http://localhost:8000 by default:

   python main.py
   

💡 Key Use Cases in AI Workflows

The Weather MCP Server can significantly enhance a variety of AI workflows, especially those involving location-based services. Here are two practical use cases:

Use Case 1: Personalized Weather Alerts for an Application

Imagine a weather app that notifies users about approaching severe weather conditions based on their current location. Using the MCP Protocol, this functionality would be integrated by setting up an MCP client that interacts with the server to fetch and process relevant weather data.

Use Case 2: Real-Time Air Quality Monitoring in Smart Cities

A smart city solution can use APIs from multiple sources to monitor air quality in real-time and make informed decisions. By integrating with the Weather MCP Server, a city management app can leverage standardized protocol calls to gather comprehensive air quality indices, improving overall public health monitoring.

🔌 Integration with MCP Clients

The Weather MCP Server supports seamless integration with several AI applications via its robust design and MCP compatibility:

• Claude Desktop: Full support for weather data operations.
• Continue: Supports all features except prompts interaction.
• Cursor: Capable of using most tools but lacks full API capabilities.

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	❌	Full Support but without prompts
Cursor	❌	✅, including astronomy data	❌	Limited to data tools only

📊 Performance & Compatibility Matrix

The Weather MCP Server is designed for high performance and compatibility across different AI clients. Key metrics include:

• Fidelity of Data: Ensures precise weather updates from multiple data sources.
• Latency: Low latency between client requests and server responses.

These factors are critical when integrating real-time or near-real-time weather data into applications.

🛠️ Advanced Configuration & Security

Advanced users can customize the configuration by modifying the main.py file. Here’s an example of how to configure MCP servers within the JSON settings:

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

Security measures can be configured to restrict access, enhance data privacy, and ensure compliance with data protection standards.

❓ Frequently Asked Questions (FAQ)

Q1: How do I configure the server for multiple MCP clients?

You can add more configurations in the mcpServers section within the settings file. Each entry corresponds to a specific client's requirements.

Q2: Are there any performance or stability issues with running this server?

Tests show that the Weather MCP Server runs smoothly, providing reliable services without significant degradation under various workloads. It is optimized for both small and large-scale deployments.

Q3: Can I use this server without Python 3.13+?

No, the minimum version stipulated is necessary to support advanced features like asynchronous operations required by FastAPI.

Q4: What data sources does this MCP server connect to?

The server primarily uses WeatherAPI, backed up with other sources for diverse and comprehensive weather information coverage.

Q5: How do I update my API key without affecting the current setup?

To update your API key, simply modify the .env file or the configuration in main.py before making any changes to existing configurations.

👨‍💻 Development & Contribution Guidelines

Contributors can help improve and extend the capabilities of this server by familiarizing themselves with the project's structure and coding standards. Detailed guidelines on setting up a development environment, testing procedures, and code submission practices are provided in the Contribution Guide.

🌐 MCP Ecosystem & Resources

Explore more about MCP and interconnected technologies through these resources:

• Documentation: Find detailed information at the official Model Context Protocol documentation.
• Community Support: Connect with fellow developers on GitHub issues or community forums dedicated to MCP integrations.

By leveraging the Weather MCP Server, AI applications can achieve better connectivity and performance in handling weather-related data, enhancing user experience and operational efficiency.