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Jiskta
by Jiskta

query_climate

Retrieve historical air quality and meteorological data for any location on Earth using Copernicus CAMS reanalysis and ERA5 data. Specify a bounding box, time range, variables, and aggregation to get CSV or stats output.

Instructions

Query historical climate and air quality data for a geographic region.

Returns Copernicus CAMS reanalysis (NO₂, PM2.5, PM10, O₃) and/or ECMWF ERA5 meteorological data (temperature, precipitation, wind, boundary layer height) for any location on Earth.

Args: lat_min: Southern boundary latitude (decimal degrees, e.g. 48.8) lat_max: Northern boundary latitude (decimal degrees, e.g. 49.0) lon_min: Western boundary longitude (decimal degrees, e.g. 2.2) lon_max: Eastern boundary longitude (decimal degrees, e.g. 2.5) time_start: Start of the period, e.g. "2022-01-01" or "2022-01" time_end: End of the period, e.g. "2023-12-31" or "2023-12" variables: Comma-separated list of variables to query. Air quality: no2, pm2p5, pm10, o3. Meteorology: era5_t2m (2m temperature), era5_tp (precipitation), era5_blh (boundary layer height), era5_u10, era5_v10 (wind). Default: "no2" aggregate: Time aggregation — "hourly", "daily", "monthly", "seasonal", or "trend" (OLS slope per year). Default: "monthly" format: Output format — "csv" (rows of data) or "stats" (summary: min/max/mean). Default: "csv"

Returns: CSV data or stats summary depending on the format argument. Credits used are shown in the response metadata.

Examples: - Air quality in Paris 2022: lat_min=48.8, lat_max=49.0, lon_min=2.2, lon_max=2.5, time_start="2022-01", time_end="2022-12", variables="no2,pm2p5" - 5-year NO₂ trend for London: aggregate="trend", time_start="2020-01", time_end="2024-12"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lat_minYes
lat_maxYes
lon_minYes
lon_maxYes
time_startYes
time_endYes
variablesNono2
aggregateNomonthly
formatNocsv

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden. It mentions credits usage in response metadata but does not explicitly state the tool is read-only or describe potential side effects, limits, or authentication requirements.

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 sections for overview, arguments, returns, and examples. It is slightly lengthy but every sentence adds value, and the format is easy to scan.

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?

Given the tool has 9 parameters, 6 required, and an output schema, the description covers all inputs, provides examples, and notes credits. It lacks details on error handling and data availability, but is otherwise complete.

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 compensates thoroughly by explaining each parameter with examples, value ranges, and default values. It clarifies the meaning of variables, aggregation options, and output formats far beyond the schema.

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 the tool queries historical climate and air quality data for a geographic region, specifying data sources (Copernicus CAMS, ERA5) and variables. It uses bounding box coordinates, distinguishing it from the sibling tool `query_climate_point` which likely queries a single point.

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?

The description provides examples and detailed parameter explanations but does not explicitly mention when to use this tool over `query_climate_point` or other alternatives. However, the context and examples imply usage for regional queries.

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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