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Retrieve the most recent observations per measure from a dataset, automatically trimming time-series to the latest period and limiting rows for large registers.

Instructions

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

For transposed time-series tables (GST_MONTHLY etc.) this trims to the most-recent period. For wide register-shaped tables (ACNC_REGISTER, IND_POSTCODE etc.) it returns the same shape as get_data, capped at limit rows. Truncated responses set DataResponse.truncated_at.

Examples: # Latest monthly net GST nationally resp = await latest("GST_MONTHLY", measures="net_gst")

# 50 charities (default cap) — narrow with filters to get specific ones
resp = await latest("ACNC_REGISTER", filters={"state": "nsw", "charity_size": "Large"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return. Register-shaped datasets (ACNC_REGISTER ~65k charities, ACNC_AIS_FINANCIALS ~50k) would otherwise blow an agent's context window. Pass filters to narrow to one entity, or raise `limit` only if you need a bulk dump. Truncated responses set DataResponse.truncated_at to the original row count so agents can detect + surface it. Time-series datasets (GST_MONTHLY etc.) already trim to the latest period and are unaffected by this cap.
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 Taxation Office
ato_urlYesClick-through URL for this dataset's source page. ato-mcp legacy name — prefer source_url (canonical) for new code. Both fields are populated identically.
recordsNo
row_countNo
dataset_idYes
source_urlYesCanonical click-through URL. Same value as ato_url; both populated for backward compat.
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/
dataset_nameYes
retrieved_atYes
stale_reasonNo
truncated_atNo
server_versionNo
Behavior5/5

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

With no annotations, the description fully carries the burden of transparency. It details behavior for two dataset types, mentions truncation via DataResponse.truncated_at, and explains the impact of the limit parameter. No contradictions occur.

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?

The description is concise with two paragraphs and a code example. No unnecessary sentences; every line contributes meaning.

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 the tool's complexity (handling two dataset types) and the presence of output schema, the description is complete. It covers behavior, examples, and limit guidance sufficiently.

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 explaining limit behavior per dataset type and truncation. References to get_data for filters and measures are adequate.

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 that the tool returns the most recent observation(s) per measure for a dataset. It distinguishes between transposed time-series tables and wide register-shaped tables, and provides concrete examples. This specificity differentiates it from siblings like get_data.

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 explains when to use the tool (for latest observations, especially for time-series) and contrasts behavior with get_data. However, it lacks explicit 'when not to use' statements or alternative tool referrals.

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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