Laundry Timer MCP

import openmeteo_requests import pandas as pd from mcp.server.fastmcp import FastMCP # Initialize FastMCP server mcp = FastMCP("laundry") @mcp.tool() async def get_fabric_types() -> list[str]: return ["light-fabrics", "typical-fabrics", "heavy-fabrics"] @mcp.tool() async def get_preferred_drying_method() -> str: return "line-drying" @mcp.tool() async def get_forecast(latitude: float, longitude: float, timezone: str) -> list[dict]: om = openmeteo_requests.AsyncClient() url = "https://api.open-meteo.com/v1/forecast" params = { "latitude": latitude, "longitude": longitude, "hourly": [ "temperature_2m", "relative_humidity_2m", "precipitation", "precipitation_probability", "wind_speed_10m", "wind_direction_10m", "uv_index" ], "timezone": timezone, } responses = await om.weather_api(url, params=params) # Process first location. Add a for-loop for multiple locations or weather models response = responses[0] # Process hourly data. The order of variables needs to be the same as requested. hourly = response.Hourly() hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy() hourly_relative_humidity_2m = hourly.Variables(1).ValuesAsNumpy() hourly_precipitation = hourly.Variables(2).ValuesAsNumpy() hourly_precipitation_probability = hourly.Variables(3).ValuesAsNumpy() hourly_wind_speed_10m = hourly.Variables(4).ValuesAsNumpy() hourly_wind_direction_10m = hourly.Variables(5).ValuesAsNumpy() hourly_uv_index = hourly.Variables(6).ValuesAsNumpy() hourly_data = { "datetime": pd.date_range( start=pd.to_datetime(hourly.Time(), unit="s", utc=True), end=pd.to_datetime(hourly.TimeEnd(), unit="s", utc=True), freq=pd.Timedelta(seconds=hourly.Interval()), inclusive="left", ), "temperature_2m": hourly_temperature_2m, "relative_humidity_2m": hourly_relative_humidity_2m, "precipitation": hourly_precipitation, "precipitation_probability": hourly_precipitation_probability, "wind_speed_10m": hourly_wind_speed_10m, "wind_direction_10m": hourly_wind_direction_10m, "uv_index": hourly_uv_index, } hourly_dataframe = pd.DataFrame(data=hourly_data) hourly_dataframe = hourly_dataframe.head(24 * 3) hourly_dataframe["datetime"] = hourly_dataframe["datetime"].dt.tz_convert(timezone) return hourly_dataframe.to_dict("records") if __name__ == "__main__": # Initialize and run the server mcp.run(transport="stdio")