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Bigred97

Australian Bureau of Statistics

latest

Retrieve the most recent observation for a dataset. Use for current-value questions like unemployment or inflation rates.

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
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.
dataset_idYesABS dataflow ID. Use search_datasets to discover.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvNo
unitNo
queryNo
staleNo
periodNo
sourceNoAustralian Bureau of Statistics
abs_urlYesClick-through URL for this dataset's source page. abs-mcp legacy name — prefer source_url (canonical) for new code. Both fields are populated identically.
recordsNo
row_countNoNumber of observation rows in records.
dataset_idYes
source_urlYesCanonical click-through URL. Same value as abs_url; both populated for backward compat.
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
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?

No annotations provided, but description details cache TTL (15 min vs 1 hour for get_data), performance metrics (p50 ~22ms warm), and warns about many observations without filters. Full disclosure of behavioral traits.

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?

Well-structured with sections for description, technical details, examples, usage guidance, and returns. Front-loaded with purpose. Every sentence adds value; no fluff.

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?

Output schema present (not shown), so return explanation not needed. Covers limitations, performance, and use cases. Examples cover multiple domains. Complete for the tool's complexity.

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%, but description adds context beyond schema: explains that filters must narrow results (e.g., 'pass at least region + measure') and provides rich examples. Adds value beyond schema.

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 explicitly states 'Return the most recent observation(s) for a dataflow' and distinguishes it from siblings like get_data (wraps with lastNObservations=1) and top_n. Examples further clarify its role.

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?

Includes a dedicated 'When to use' section listing scenarios (current value, performance) and implies alternatives (get_data for more observations). Clear guidance on when to apply.

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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