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get_data

Query curated Australian Taxation Office and ACNC charity datasets to retrieve observations filtered by state, postcode, industry, or charity size. Returns records, series, or CSV with attribution.

Instructions

Query a curated ATO/ACNC dataset and return observations.

Examples: # Median taxable income in postcode 2000 (Sydney CBD), 2022-23 resp = await get_data( "IND_POSTCODE_MEDIAN", filters={"state": "nsw", "postcode": "2000"}, measures="median_taxable_income_2022_23", )

# All registered charities in NSW with size = "large"
resp = await get_data(
    "ACNC_REGISTER",
    filters={"state": "NSW", "charity_size": "Large"},
    measures=["total_gross_income", "total_employees"],
)

# 2023-24 corporate tax payable for entities with total income > $1B
resp = await get_data("CORP_TRANSPARENCY", filters={"income_year": "2023-24"})

Returns: DataResponse with records (or csv), unit, period bounds, row_count, source URL, and CC-BY attribution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesCurated dataset ID. Use search_datasets() / list_curated().
filtersNoDimension filters. Keys are plain-English aliases from the dataset's describe_dataset response. Values are matched against the source data; pass a list to OR across values. Examples: {'state': 'nsw'}, {'postcode': '2000'}, {'industry_broad': ['A', 'B']}.
measuresNoWhich measure(s) to return. Plain-English keys from describe_dataset. Omit to return all measures.
start_periodNoInclusive start period for transposed time-series datasets (GST_MONTHLY etc). Ignored for wide single-year tables. Format: 'YYYY' or 'YYYY-MM'.
end_periodNoInclusive end period. Same format as start_period.
formatNoResponse shape. 'records' (default): flat list of observations. 'series': grouped by measure. 'csv': pandas CSV string in `csv` field.records

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYes
dataset_nameYes
queryNo
periodNo
unitNo
row_countNo
recordsNo
csvNo
sourceNoAustralian Taxation Office
attributionNoData sourced from the Australian Taxation Office (and, for charity data, the Australian Charities and Not-for-profits Commission) via data.gov.au. Licensed under Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU). https://creativecommons.org/licenses/by/3.0/au/
retrieved_atYes
ato_urlYes
server_versionNo
Behavior3/5

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

No annotations exist, so the description bears full burden. It describes the return structure (records, csv, unit, etc.) and provides examples, but does not discuss potential side effects, error conditions, rate limits, or performance characteristics.

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?

Description is well-structured with a purpose sentence, examples, and return info. Examples are helpful but add length; still concise enough for the tool's 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?

Given 6 parameters, high schema coverage, and no output schema (but return type described), the description covers purpose, parameter usage via examples, and return format. It is sufficient for typical use, though could include error handling notes.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by giving concrete examples for each parameter in the description text and referencing search_datasets() for dataset_id, going beyond the schema alone.

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?

Description clearly states the tool 'Query a curated ATO/ACNC dataset and return observations.' The verb 'query' and resource 'curated dataset' are specific, and the ATO/ACNC context differentiates it from sibling tools like list_curated and search_datasets which deal with metadata.

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?

Examples show typical usage and mention searching for dataset IDs via search_datasets(), providing some guidance. However, it does not explicitly state when not to use this tool or compare with alternatives like describe_dataset for schema exploration.

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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