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chuk-mcp-open-meteo

by IBM

batch_get_weather_forecasts

Retrieve weather forecasts for multiple locations simultaneously using a single API call, supporting up to 1000 locations for faster data collection.

Instructions

Get weather forecasts for multiple locations in a single API call.

This tool uses Open-Meteo's native batch support to fetch forecasts for up to 1000 locations in ONE HTTP request. This is dramatically faster than calling get_weather_forecast repeatedly.

Use batch_geocode_locations first to get coordinates, then pass them here.

Args: latitudes: Comma-separated latitude values for all locations. Example: "51.51,48.86,52.52" (London, Paris, Berlin) Must have the same number of values as longitudes. longitudes: Comma-separated longitude values for all locations. Example: "-0.13,2.35,13.41" (London, Paris, Berlin) Must have the same number of values as latitudes. temperature_unit: Temperature unit - "celsius" (default) or "fahrenheit" wind_speed_unit: Wind speed unit - "kmh" (default), "ms", "mph", "kn" precipitation_unit: Precipitation unit - "mm" (default) or "inch" timezone: Timezone name or "auto" for automatic detection per location forecast_days: Number of forecast days (1-16). Default is 7. current_weather: Include current weather conditions. Default is True. hourly: Comma-separated hourly variables (same as get_weather_forecast). Popular: temperature_2m, precipitation, wind_speed_10m, cloud_cover daily: Comma-separated daily variables (same as get_weather_forecast). Popular: temperature_2m_max, temperature_2m_min, precipitation_sum

Returns: BatchWeatherForecastResponse: Contains: - results: List of BatchWeatherForecastItem, each with location_index and forecast - total_locations: Number of locations queried

Tips for LLMs: - ALWAYS use batch_geocode_locations first to get coordinates - The forecasts are returned in the SAME ORDER as the input coordinates - All locations share the same forecast parameters (hourly, daily, units) - For different parameters per location, make separate get_weather_forecast calls - Maximum ~1000 locations per call (Open-Meteo API limit) - The latitudes and longitudes strings must have equal numbers of comma-separated values

Workflow for "What's the weather across the UK?": 1. batch_geocode_locations("London,Manchester,Edinburgh,Cardiff,Belfast,Birmingham") 2. Extract latitudes and longitudes from successful results 3. batch_get_weather_forecasts(latitudes="51.51,53.48,55.95,...", longitudes="-0.13,-2.24,-3.19,...") 4. Present the weather comparison to the user

Example: # Get weather for London, Paris, and Berlin forecasts = await batch_get_weather_forecasts( latitudes="51.51,48.86,52.52", longitudes="-0.13,2.35,13.41", current_weather=True, daily="temperature_2m_max,temperature_2m_min,precipitation_sum" ) for item in forecasts.results: f = item.forecast print(f"Location {item.location_index}: {f.current_weather.temperature}C")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dailyNo
hourlyNo
timezoneNoauto
latitudesYes
longitudesYes
forecast_daysNo
current_weatherNo
wind_speed_unitNokmh
temperature_unitNocelsius
precipitation_unitNomm
Behavior5/5

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

No annotations provided, but the description fully discloses behavior: uses Open-Meteo native batch support, returns results in input order, shares parameters across locations, and is faster than repeated calls.

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?

The description is well-structured with clear sections but is somewhat lengthy. However, every sentence adds value, and the format is easy to parse.

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?

Given 10 parameters, no output schema, and no annotations, the description is comprehensive: explains return type, workflow, constraints, and provides a full example. It leaves no significant gaps.

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 description coverage is 0%, but the description provides detailed explanations for all parameters, including examples, defaults, and the constraint that latitudes and longitudes must have equal comma-separated values.

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?

The description clearly states it gets weather forecasts for multiple locations in a single API call, distinguishing it from get_weather_forecast for single locations and mentioning batch geocoding as a prerequisite.

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

Usage Guidelines5/5

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

Explicitly advises using batch_geocode_locations first, explains when to use this tool vs get_weather_forecast (batch vs different params per location), and notes the API limit of ~1000 locations.

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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