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Bigred97

Australian Institute of Health and Welfare

get_data

Query curated AIHW health datasets to retrieve mortality, cancer, hospital, and expenditure observations with optional filters and time periods.

Instructions

Query a curated AIHW dataset and return observations.

Examples: # Deaths from diabetes, all years and sexes resp = await get_data( "GRIM_DEATHS", filters={"cause_of_death": "Diabetes"}, measures="deaths", )

# Breast cancer incidence in females over time
resp = await get_data(
    "CANCER_INCIDENCE_MORTALITY",
    filters={"cancer_type": "Breast cancer", "sex": "Female", "type": "Incidence"},
)

# Public hospitals in NSW with peer group "Principal referral"
resp = await get_data(
    "PUBLIC_HOSPITALS",
    filters={"state": "NSW", "peer_group_name": "Principal referral"},
)

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: {'sex': 'female'}, {'year': '2023'}, {'cause_of_death': ['Diabetes', 'Stroke']}.
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. 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 Institute of Health and Welfare
attributionNoData sourced from the Australian Institute of Health and Welfare (AIHW) 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
aihw_urlYes
server_versionNo
Behavior4/5

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

With no annotations, the description fully carries the burden. It clearly states the return type (DataResponse) and its fields (records, csv, unit, period bounds, row_count, source URL, CC-BY attribution). Examples show typical usage and output structure. No destructive or side-effect behaviors are implied. The description does not conflict with any missing annotations.

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

Conciseness3/5

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

The description is somewhat lengthy due to multiple examples and a return specification block. However, the examples are illustrative and aid understanding. The structure is front-loaded with the core purpose, but the examples could be truncated if brevity were paramount. Not overly verbose for the 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 the tool's complexity (6 params, 1 required), high schema coverage, and presence of an output schema, the description covers the essentials well. It explains filters, measures, period boundaries, and output format. The examples fill practical gaps. Missing details like error handling or performance are minor given the overall richness.

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 coverage is 100%—all 6 parameters are documented with descriptions and examples. The description adds value beyond the schema by explaining that 'filters' use 'plain-English aliases from describe_dataset', and 'measures' are from the same source. These are crucial semantic clarifications not inferable from type 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?

The description starts with a clear verb-object pair: 'Query a curated AIHW dataset and return observations.' It specifies the resource (AIHW dataset) and action (query). The examples further clarify the data type. Among siblings (describe_dataset, latest, list_curated, search_datasets, top_n), this tool's purpose is distinct—it retrieves observations based on filters, not metadata or summaries.

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

Usage Guidelines3/5

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

The description provides usage contexts via examples, implicitly showing when to use this tool (to query specific datasets with filters). However, it does not explicitly state alternatives for cases like metadata retrieval (describe_dataset) or listing available datasets (list_curated). No direct 'when not to use' guidance is given.

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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