AQICN MCP Server: Real-Time Air Quality Data Integration

Overview: What is AQICN MCP Server?

AQICN (Air Quality Index China) is a world-renowned platform providing real-time air quality data for cities and coordinates worldwide. This AQICN MCP Server acts as an adapter, bridging the GapAI (Claude Desktop, Continue, Cursor) ecosystem with the vast database of air quality information hosted by AQICN. By adhering to the Model Context Protocol (MCP), this server ensures seamless integration and usage of AQICN’s data through standardized tools.

🔧 Core Features & MCP Capabilities

The AQICN MCP Server is built around providing developers and AI applications with real-time air quality data in a structured, standardized manner. Key features include:

• City-Level Data: Fetch current air quality index (AQI) for specific cities.
• Geospatial Data: Retrieve AQI using geographical coordinates.
• Station Search: Identify nearby or relevant air quality monitoring stations based on keywords.

These features are delivered through MCP tools, allowing AI applications to interact with the server in a contextually rich manner. The underlying implementation of these tools adheres strictly to the MCP protocol, ensuring compatibility and interoperability across different platforms such as GapAI.

⚙️ MCP Architecture & Protocol Implementation

The AQICN MCP Server is designed to seamlessly integrate into the broader MCP ecosystem by following rigorous architectural standards:

• Tool Registration: Tools like city_aqi, geo_aqi, and search_station are registered with the protocol, each providing a specific function related to air quality data.
• Data Transformation: The server processes incoming requests according to defined schemas and transforms raw AQICN API responses into structured MCP tool outputs.
• Error Handling & Logging: Robust error handling ensures that any issues during data retrieval or transformation are logged and managed gracefully.

Sample Implementation of city_aqi Tool

from mcp import make_tool, context
from .aqicn_api_client import AirQualityClient

@applicability(context.city)
@make_tool("city_aqi", description="Get air quality data for a specific city.")
def city_aqi(city: str) -> AQIData:
    """Retrieve and return the current air quality index for the given city."""
    client = AirQualityClient()
    return await client.fetch_city_data(city)

This snippet illustrates how city_aqi tool is integrated into the server, leveraging an internal API client to fetch relevant data.

🚀 Getting Started with Installation

To deploy and utilize the AQICN MCP Server within your AI environments, follow these steps:

Installing via Smithery (Automatic Integration)

npx -y @smithery/cli install @mattmarcin/aqicn-mcp --client claude

This command initializes the server as a CLI package for GapAI desktop clients.

Installing via uV (Manual Setup)

For developers who prefer a hands-on approach, initialize an isolated Python environment using uv:

# Install and activate virtual environment
uv pip install -e .

💡 Key Use Cases in AI Workflows

1. Environmental Monitoring: AI-powered systems can integrate real-time air quality data into dashboards or predictive models to monitor environmental changes.
2. Public Health Insights: AI applications can analyze historical AQI data to provide public health advisories during periods of high pollution.

Real-World Workflow Example: Public Health Advisory System

Consider an AI-based public health advisory system that needs to alert residents in cities with poor air quality:

import mcp, asyncio

async def monitor_air_quality():
    async with Client() as client:
        while True:
            beijing_data = await client.city_aqi(city="Beijing")
            if beijing_data.aqi > 150:
                print("High pollution alert! Advising residents to stay indoors.")
            
            # Add more cities and conditions based on your use-case
            # await asyncio.sleep(3600)  # Sleep for an hour before next check

monitor_air_quality()

The above script demonstrates a practical integration of AQICN MCP Server’s city_aqi tool to create real-time pollution alerts.

🔌 Integration with MCP Clients

The following table outlines compatibility and support status across different MCP clients:

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

The table clearly indicates that while Cursor currently supports only tool integrations, both Claude Desktop and Continue fully support resource, prompt, and tool interactions.

📊 Performance & Compatibility Matrix

MCP Protocol Flow Diagram

graph TB
    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 Client Compatibility Matrix

| MCP Client | Resources | Tools | Prompts |
|------------|-----------|-------|---------|
| Claude Desktop | ✅ | ✅ | ✅ | Full Support |
| Continue | ✅ | ✅ | ✅ | Full Support |
| Cursor | ❌ | ✅ | ❌ | Tool Only |

🛠️ Advanced Configuration & Security

For detailed configuration and security considerations, refer to the mcp.config file included in the project directory. This guide covers:

• Environment Variables: Set with API keys and other sensitive data.
• Security Settings: Best practices for securing the server while enabling seamless API access.

Sample mcp.config

{
  "mcpServers": {
    "[aqicn-mcp-server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-aqicn"],
      "env": {
        "AQICN_API_KEY": "your_api_key_here"
      }
    }
  }
}

❓ Frequently Asked Questions (FAQ)

1. Q: Why should I integrate AQICN with MCP?

   • A: The integration allows seamless real-time air quality data access directly within your AI applications.

2. Q: Which GapAI clients are compatible with this server?

   • A: Fully supported by Claude Desktop and Continue, while Cursor only supports tools at the moment.

3. Q: How do I handle security when integrating MCP servers in my applications?

   • A: Use environment variables for sensitive information and apply standard security practices to protect your data.

4. Q: Can this server be used with custom clients?

   • A: Yes, but you may need to adjust configuration based on the client’s requirements.

5. Q: What kind of error handling should I expect from this tool?

   • A: The AQICN server implements thorough error handling and logging mechanisms for any issues encountered during operations.

👨‍💻 Development & Contribution Guidelines

Contributions to improve the AQICN MCP Server are welcome! To contribute, follow these guidelines:

• Fork the repository.
• Create a new branch with appropriate naming conventions.
• Develop your feature or bug fix.
• Ensure comprehensive testing of changes.
• Open a pull request with detailed explanations.

🌐 MCP Ecosystem & Resources

For more information and resources on MCP servers and their applications, refer to the official MCP documentation and community forums:

• MCP Documentation: [Link]
• Community Forum: [Link]

The AQICN MCP Server stands as an essential tool for developers and AI application integrators looking to leverage real-time air quality data in a robust and standardized manner.