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Bigred97

Australian Institute of Health and Welfare

get_data

Retrieve observations from curated AIHW datasets by applying filters on dimensions, measures, and time periods. Output as records, series, or CSV.

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
formatNoResponse shape. 'records' (default): flat list of observations. 'series': grouped by measure. 'csv': pandas CSV string in `csv` field.records
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.
dataset_idYesCurated dataset ID. Use the search or list-curated endpoint/tool to discover.
end_periodNoInclusive end period. Same format as start_period.
start_periodNoInclusive start period for transposed time-series datasets. Ignored for wide single-year tables. Format: 'YYYY' or 'YYYY-MM' or AIHW FY 'YYYY-YY'. Bare int years like 2010 are coerced to '2010' automatically.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvNo
unitNo
queryNo
staleNo
periodNo
sourceNoAustralian Institute of Health and Welfare
recordsNo
aihw_urlYesClick-through URL for this dataset's source page. aihw-mcp legacy name — prefer source_url (canonical) for new code. Both fields are populated identically.
row_countNo
dataset_idYes
source_urlYesCanonical click-through URL. Same value as aihw_url; both populated for backward compat.
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/
dataset_nameYes
retrieved_atYes
stale_reasonNo
truncated_atNo
server_versionNo
Behavior3/5

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

No annotations provided; description covers return structure and filter behavior (OR across list values) but lacks info on rate limits, auth, or error handling. Decent but not comprehensive.

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?

Concise summary, illustrative examples, and return description—every element earns its place. Front-loaded purpose.

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?

Covers all key parameters (dataset_id, filters, measures, format, periods) and references describe_dataset for more detail. Output schema is mentioned, making it complete for a data query tool.

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?

100% schema coverage gives baseline 3; description adds value with examples, notes on plain-English aliases, and period formats, slightly exceeding 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?

The description clearly states it queries a curated AIHW dataset and returns observations, with specific verb and resource. Examples differentiate from siblings like search_datasets and latest.

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?

Examples imply usage, but no explicit when-to-use or when-not-to-use compared to siblings. No alternatives mentioned.

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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