cooper-hewitt-mcp

# AQICN MCP Server [](https://smithery.ai/server/@mattmarcin/aqicn-mcp) 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](https://smithery.ai/server/@mattmarcin/aqicn-mcp): ```bash npx -y @smithery/cli install @mattmarcin/aqicn-mcp --client claude ``` ### Installing via recommended uv (manual) We recommend using [uv](https://docs.astral.sh/uv/) to manage your Python environment: ```bash # 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`): ```bash # .env AQICN_API_KEY=your_api_key_here ``` Alternatively, you can set the environment variable directly: ```bash # 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: ```bash mcp dev aqicn_server.py ``` ### Claude Desktop Integration Once your server is ready, install it in Claude Desktop: ```bash mcp install aqicn_server.py ``` ### Direct Execution For testing or custom deployments: ```bash python aqicn_server.py ``` ## Available Tools ### 1. city_aqi Get air quality data for a specific city. ```python @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 value - `station`: Station name - `dominant_pollutant`: Main pollutant (if available) - `time`: Timestamp of the measurement - `coordinates`: Latitude and longitude of the station ### 2. geo_aqi Get air quality data for a specific location using coordinates. ```python @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 location - `longitude`: Longitude of the location **Output:** Same as `city_aqi` ### 3. search_station Search for air quality monitoring stations by keyword. ```python @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 name - `station_id`: Unique station identifier - `coordinates`: Latitude and longitude of the station ## Example Usage Using the MCP Python client: ```python 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.