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IBM

chuk-mcp-open-meteo

by IBM

get_weather_forecast

Retrieve detailed weather forecasts including current conditions, hourly, and daily data for any location. Specify latitude, longitude, and optional parameters to customize units, timezone, and forecast days.

Instructions

Get comprehensive weather forecast with current conditions, hourly, and daily forecasts.

This tool provides detailed weather forecasts from Open-Meteo API with 50+ weather variables. Use this for answering questions about current weather, future forecasts, or detailed conditions.

Args: latitude: Latitude coordinate in decimal degrees (-90 to 90). Use geocode_location to find coordinates. longitude: Longitude coordinate in decimal degrees (-180 to 180). Use geocode_location to find coordinates. temperature_unit: Temperature unit. Options: "celsius" (default), "fahrenheit" wind_speed_unit: Wind speed unit. Options: "kmh" (default), "ms", "mph", "kn" precipitation_unit: Precipitation unit. Options: "mm" (default), "inch" timezone: Timezone name (e.g., "America/New_York", "Europe/London") or "auto" for automatic detection forecast_days: Number of forecast days (1-16). Default is 7. current_weather: Set to True to include current weather conditions (recommended) hourly: Comma-separated list of hourly variables. Popular options: - temperature_2m: Temperature at 2m height - precipitation: Total precipitation (rain + snow) - rain: Rain only - snowfall: Snowfall amount - cloud_cover: Cloud cover percentage (0-100) - wind_speed_10m, wind_direction_10m: Wind at 10m height - relative_humidity_2m: Relative humidity - pressure_msl: Sea level pressure - visibility: Visibility distance - uv_index: UV index daily: Comma-separated list of daily variables. Popular options: - temperature_2m_max, temperature_2m_min: Daily temperature range - precipitation_sum: Total daily precipitation - rain_sum: Total daily rain - sunrise, sunset: Sun times - wind_speed_10m_max: Maximum daily wind speed - precipitation_hours: Hours with precipitation

Returns: WeatherForecast: A Pydantic model containing: - latitude, longitude: Actual coordinates used - current_weather: Current conditions (temperature, wind, weather code) - hourly: Hourly forecast data (if requested) - daily: Daily forecast data (if requested) - timezone: Timezone information

Tips for LLMs: - Always use current_weather=True for "what's the weather" questions - Request hourly data for detailed forecasts (e.g., "hourly rain predictions") - Request daily data for multi-day forecasts (e.g., "week ahead") - Weather codes: 0=clear, 1-3=partly cloudy, 45/48=fog, 51-57=drizzle, 61-67=rain, 71-77=snow, 80-82=rain showers, 95-99=thunderstorm

Example: # Get current weather for London forecast = await get_weather_forecast(51.5072, -0.1276, current_weather=True) temp = forecast.current_weather.temperature

# Get detailed 3-day forecast with hourly data
forecast = await get_weather_forecast(
    51.5072, -0.1276,
    forecast_days=3,
    hourly="temperature_2m,precipitation,wind_speed_10m",
    daily="temperature_2m_max,temperature_2m_min,precipitation_sum"
)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dailyNo
hourlyNo
latitudeYes
timezoneNoauto
longitudeYes
forecast_daysNo
current_weatherNo
wind_speed_unitNokmh
temperature_unitNocelsius
precipitation_unitNomm
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Describes return model, mentions Open-Meteo API, and weather codes. No mention of side effects or destructive behavior, but it's a read-only forecast.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with sections (overview, usage, args, returns, tips, examples). Slightly long but each part adds value. Front-loaded with purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but return object detailed. 10 parameters all documented. Usage tips and examples provided. Complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description thoroughly explains all 10 parameters with defaults, options, and examples. Includes popular hourly/daily variables and tips like using geocode_location for coordinates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Get comprehensive weather forecast with current conditions, hourly, and daily forecasts.' Distinguishes from sibling tools like get_marine_forecast, get_air_quality, get_historical_weather.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

States 'Use this for answering questions about current weather, future forecasts, or detailed conditions.' Provides tips for LLMs on when to use current_weather, hourly, daily. No explicit exclusions but context clear given distinct siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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