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Bigred97

Australian Bureau of Statistics

latest

Retrieve the most recent value of any ABS indicator (e.g., unemployment rate, inflation) using plain-English names. Returns a single current observation with minimal latency.

Instructions

Return the most recent observation(s) for a dataflow.

Wraps get_data with lastNObservations=1 and a 15-minute cache TTL (vs 1 hour for general data calls). Use this for "what's the current X?" questions — it's a cheap, fast call: warm-cache p50 ~22ms, cold-cache ~200ms.

Examples: # Latest NSW unemployment rate resp = await latest("LF", {"region": "nsw", "measure": "unemployment_rate"}) # → resp.records[0]: period='2026-03', value=4.61, unit='Percent'

# Latest Australia headline annual inflation
resp = await latest("CPI", {"region": "australia", "measure": "change_year"})
# → resp.records[0]: period='2026-Q1', value=4.6, unit='Percent'

# Latest Greater Sydney population
resp = await latest("ABS_ANNUAL_ERP_ASGS2021",
                    {"region": "greater_sydney", "region_type": "gccsa"})
# → resp.records[0]: period='2025', value=5640000, unit='Persons'

When to use: - You want "the current value" of an indicator (most common workflow) - You're answering a "what's the unemployment rate?" style question - You want sub-50ms warm-cache latency for chat/agent integration

Returns: DataResponse with one most-recent observation per matched dimension combination. Same envelope as get_data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesABS dataflow ID. Use search_datasets to discover.
filtersNoDimension filters. For curated dataflows: plain-English keys and values. Without filters, expect one observation per dimension combination (often hundreds) — pass at least region + measure for a clean single number.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYes
dataset_nameYes
queryNo
periodNo
unitNo
recordsNo
csvNo
sourceNoAustralian Bureau of Statistics
attributionNoData sourced from the Australian Bureau of Statistics and licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). https://www.abs.gov.au/about/copyright-and-creative-commons
retrieved_atYes
abs_urlYes
server_versionNo
Behavior4/5

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

With no annotations, the description fully bears the burden of disclosure. It explains the 15-minute cache TTL (vs 1 hour for general calls), performance latencies, and that it wraps get_data with lastNObservations=1. It does not mention any destructive behavior, rate limits, or auth requirements, but these are likely not applicable.

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, well-structured with clear sections (purpose, explanation, examples, when to use, returns). It front-loads the core purpose and uses examples to convey usage efficiently. Every sentence adds value without redundancy.

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 relative simplicity (2 parameters, no enums, output schema exists), the description is remarkably complete. It covers purpose, behavior, performance, filtering guidance, and output format. It references the sibling tool get_data for context and provides comprehensive examples.

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?

The input schema already provides descriptions and examples for both parameters (100% coverage). The description reinforces by showing actual usage patterns and expected results, which adds value beyond the schema. It demonstrates how filters work together to produce a single observation.

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) for a dataflow. It distinguishes itself from siblings like get_data by noting it wraps it with lastNObservations=1 and a shorter cache TTL. The phrase 'Use this for what's the current X? questions' further clarifies its specific purpose.

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

Usage Guidelines5/5

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

The description includes a dedicated 'When to use' section that explicitly lists scenarios (e.g., wanting the current value, answering simple indicator questions, needing low latency). It also implies when not to use by contrasting with get_data for historical data. The examples illustrate typical use cases.

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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