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chuk-mcp-open-meteo

by IBM

batch_get_air_quality

Fetch air quality data for multiple locations simultaneously to compare pollution levels across cities.

Instructions

Get air quality data for multiple locations in a single API call.

This tool uses Open-Meteo's native batch support to fetch air quality data for many locations at once. Useful for comparing pollution levels across cities.

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. timezone: Timezone name or "auto" for automatic detection per location hourly: Comma-separated air quality variables. If not provided, defaults to: pm10, pm2_5, carbon_monoxide, nitrogen_dioxide, sulphur_dioxide, ozone, us_aqi, european_aqi domains: Model domain - "auto" (default), "cams_global", "cams_europe"

Returns: BatchAirQualityResponse: Contains: - results: List of BatchAirQualityItem, each with location_index and air_quality - total_locations: Number of locations queried

Tips for LLMs: - Use batch_geocode_locations first to get coordinates - Results are in the SAME ORDER as the input coordinates - Useful for "compare air quality across cities" queries - All locations share the same hourly variables and domain settings

Example: # Compare air quality across 3 cities result = await batch_get_air_quality( latitudes="51.51,48.86,34.05", longitudes="-0.13,2.35,-118.24", hourly="pm2_5,us_aqi" ) for item in result.results: aqi = item.air_quality.hourly.us_aqi[0] print(f"Location {item.location_index}: AQI {aqi}")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hourlyNo
domainsNoauto
timezoneNoauto
latitudesYes
longitudesYes
Behavior4/5

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

No annotations provided, so description carries burden. Discloses that results are in same order as input, all locations share settings. Does not mention destructive actions (none expected) or rate limits; could add data freshness or failure handling.

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

Conciseness5/5

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

Well-organized with sections: description, args, returns, tips, example. Every sentence adds value. No unnecessary text. Length appropriate for complexity.

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

Completeness4/5

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

No output schema, but description explains return structure (BatchAirQualityResponse with results and total_locations). Includes example usage. Could be slightly more thorough on error cases or limits, but adequate for agent invocation.

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 has 0% coverage, but description provides full parameter details: format (comma-separated), examples, constraints (same count for lat/lng), defaults, and options for hourly and domains. This compensates completely for missing schema 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?

Clear verb 'Get air quality data' with specific resource 'multiple locations'. Differentiates from sibling tools like get_air_quality (single location) and batch_geocode_locations (geocoding). Explicitly states use case: comparing pollution across cities.

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 states when to use: for multiple locations in one call, comparing across cities. Provides tip to use batch_geocode_locations first. Implicitly contrasts with single-location tool. No misleading 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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