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chuk-mcp-open-meteo

by IBM
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

batch_get_historical_weather

Retrieve historical weather data for multiple locations in one request, enabling climate comparisons across cities over the same time period.

Instructions

Get historical weather data for multiple locations in a single API call.

This tool uses Open-Meteo's native batch support to fetch historical weather for many locations at once. All locations share the same date range.

Args: latitudes: Comma-separated latitude values for all locations. Example: "51.51,48.86,52.52" Must have the same number of values as longitudes. longitudes: Comma-separated longitude values for all locations. Example: "-0.13,2.35,13.41" Must have the same number of values as latitudes. start_date: Start date in YYYY-MM-DD format (shared across all locations) end_date: End date in YYYY-MM-DD format (shared across all locations) 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 hourly: Comma-separated hourly variables (same as get_historical_weather). daily: Comma-separated daily variables (same as get_historical_weather).

Returns: BatchHistoricalWeatherResponse: Contains: - results: List of BatchHistoricalWeatherItem, each with location_index and weather - total_locations: Number of locations queried

Tips for LLMs: - Use batch_geocode_locations first to get coordinates - All locations share the same date range - if you need different dates per location, use separate get_historical_weather calls - Results are in the SAME ORDER as the input coordinates - Useful for climate comparisons across cities for the same time period

Example: # Compare last week's weather across European capitals result = await batch_get_historical_weather( latitudes="51.51,48.86,52.52", longitudes="-0.13,2.35,13.41", start_date="2025-01-01", end_date="2025-01-07", daily="temperature_2m_max,temperature_2m_min,precipitation_sum" )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dailyNo
hourlyNo
end_dateYes
timezoneNoauto
latitudesYes
longitudesYes
start_dateYes
wind_speed_unitNokmh
temperature_unitNocelsius
precipitation_unitNomm

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

No annotations are provided, so the description fully bears the burden. It discloses that all locations share the same date range and units, that results maintain input order, and describes the return structure. It also includes tips and examples, ensuring transparency.

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-organized with sections (Args, Returns, Tips, Example) and covers all necessary information. While it is thorough, it could be slightly more concise without losing clarity. Still, it earns a 4 for its structure.

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 the tool's complexity (10 parameters, no output schema, no annotations), the description is remarkably complete. It explains return format, behavior, constraints, and includes a concrete example, leaving no gaps for an agent.

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?

Input schema has 0% description coverage, yet the description provides comprehensive parameter details: examples, constraints (e.g., comma-separated, equal lengths), defaults, and allowable values. It fully compensates for the schema's lack of descriptions.

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 that the tool fetches historical weather data for multiple locations in a single API call. It distinguishes itself from the sibling tool 'get_historical_weather' by emphasizing the batch capability, and from other batch tools by specifying the weather domain.

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?

The description explicitly states when to use this tool (multiple locations, same date range) and when not to (different dates per location → use separate calls). It also recommends using batch_geocode_locations first, providing clear guidance.

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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