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Bigred97

Australian Institute of Health and Welfare

latest

Return the most recent data values for a given dataset. Default filters show the national headline figure; override filters to get observations for specific states or causes of death.

Instructions

Return the most recent observation(s) per measure for a dataset.

For transposed time-series tables this trims to the most-recent period. For wide single-year tables (most AIHW datasets) it returns the same shape as get_data — there is only one period in those tables.

When the curated YAML declares a headline_slice, those filter values are applied automatically so the no-filter call returns ONE canonical headline row (e.g. HEALTH_EXPENDITURE → national total, all areas, all sources). User-supplied filters override the headline_slice per-key — latest("HEALTH_EXPENDITURE", filters={"state": "NSW"}) keeps the area/source defaults and returns the NSW latest-year total.

Examples: # Latest year of GRIM data for All causes combined resp = await latest("GRIM_DEATHS", filters={"cause_of_death": "All causes combined"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersNoSame filter shape as get_data. Useful for narrowing to one entity.
measuresNoSame as get_data.
dataset_idYesCurated dataset ID.

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
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses key behaviors: trimming to most-recent period, headline_slice defaults, user overrides. Does not cover auth or rate limits, but overall good.

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 front-loaded with main purpose, then explains nuances. Examples are helpful. Could be slightly more concise, but well-structured.

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?

Given output schema exists and moderate complexity, description is thorough. Explains headline_slice, table type behavior, and provides examples. Complete for agent use.

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

Parameters3/5

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

Schema coverage is 100%, so baseline 3. Description adds little beyond schema for parameters, only notes that filters are same shape as get_data and gives examples. No extra detail for measures.

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 states the tool returns the most recent observation(s) per measure for a dataset. It distinguishes from siblings like get_data by focusing on latest data and explains behavior for different table types.

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?

Explains when to use for transposed vs wide tables, and how headline_slice applies. Provides examples. Lacks explicit when-not-to-use but context is clear.

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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