About AQICN MCP Server
AQICN MCP Server provides air quality data from the World Air Quality Index project to LLMs. It supports lookups by city or coordinates and searches for monitoring stations.
Key features
- 01Get AQI data by city
- 02Get AQI data by coordinates
- 03Search monitoring stations
- 04Return pollutant and measurement details
Use cases
- 01Check air quality in a city
- 02Look up AQI near a location
- 03Find nearby monitoring station details
MCP support
The MCP server exposes AQICN tools for city and coordinate-based air quality lookups, as well as monitoring-station searches.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEAQICN MCP Server
This is a Model Context Protocol (MCP) server that provides air quality data tools from the World Air Quality Index (AQICN) project. It allows LLMs to fetch real-time air quality data for cities and coordinates worldwide.
Installation
Installing via Smithery
To install AQICN MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @mattmarcin/aqicn-mcp --client claude
Installing via recommended uv (manual)
We recommend using uv to manage your Python environment:
# Install the package and dependencies
uv pip install -e .
Environment Setup
Create a .env file in the project root (you can copy from .env.example):
# .env
AQICN_API_KEY=your_api_key_here
Alternatively, you can set the environment variable directly:
# Linux/macOS
export AQICN_API_KEY=your_api_key_here
# Windows
set AQICN_API_KEY=your_api_key_here
Running the Server
Development Mode
The fastest way to test and debug your server is with the MCP Inspector:
mcp dev aqicn_server.py
Claude Desktop Integration
Once your server is ready, install it in Claude Desktop:
mcp install aqicn_server.py
Direct Execution
For testing or custom deployments:
python aqicn_server.py
Available Tools
1. city_aqi
Get air quality data for a specific city.
@mcp.tool()
def city_aqi(city: str) -> AQIData:
"""Get air quality data for a specific city."""
Input:
city: Name of the city to get air quality data for
Output: AQIData with:
aqi: Air Quality Index valuestation: Station namedominant_pollutant: Main pollutant (if available)time: Timestamp of the measurementcoordinates: Latitude and longitude of the station
2. geo_aqi
Get air quality data for a specific location using coordinates.
@mcp.tool()
def geo_aqi(latitude: float, longitude: float) -> AQIData:
"""Get air quality data for a specific location using coordinates."""
Input:
latitude: Latitude of the locationlongitude: Longitude of the location
Output: Same as city_aqi
3. search_station
Search for air quality monitoring stations by keyword.
@mcp.tool()
def search_station(keyword: str) -> list[StationInfo]:
"""Search for air quality monitoring stations by keyword."""
Input:
keyword: Keyword to search for stations (city name, station name, etc.)
Output: List of StationInfo with:
name: Station namestation_id: Unique station identifiercoordinates: Latitude and longitude of the station
Example Usage
Using the MCP Python client:
from mcp import Client
async with Client() as client:
# Get air quality data for Beijing
beijing_data = await client.city_aqi(city="beijing")
print(f"Beijing AQI: {beijing_data.aqi}")
# Get air quality data by coordinates (Tokyo)
geo_data = await client.geo_aqi(latitude=35.6762, longitude=139.6503)
print(f"Tokyo AQI: {geo_data.aqi}")
# Search for stations
stations = await client.search_station(keyword="london")
for station in stations:
print(f"Station: {station.name} ({station.coordinates})")
Contributing
Feel free to open issues and pull requests. Please ensure your changes include appropriate tests and documentation.
License
This project is licensed under the MIT License.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.