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Bigred97

Australian Bureau of Statistics

list_curated

Returns a sorted list of 10 ABS dataflow IDs that accept plain-English filter keys and provide rich metadata for key economic indicators like unemployment, inflation, wages, GDP, housing, and population.

Instructions

List the 10 ABS dataflow IDs with hand-curated plain-English support.

These are the dataflows where get_data accepts plain-English filter keys ({"region": "nsw"}) and describe_dataset returns rich human-readable metadata. All other ABS dataflows (~1,200) are still accessible via get_data with raw SDMX dimension IDs and codes.

The 10 curated dataflows: - LF — Labour Force (unemployment, employment, participation) - CPI — Consumer Price Index (inflation) - WPI — Wage Price Index (wage growth) - AWE — Average Weekly Earnings - JV — Job Vacancies - BA_GCCSA — Building Approvals (by Greater Capital City) - LEND_HOUSING — Lending Indicators / Housing Finance - ANA_AGG — National Accounts (GDP) - ERP_Q — Estimated Resident Population (quarterly) - ABS_ANNUAL_ERP_ASGS2021 — Population (annual; supports SA2/SA3/SA4)

Example: ids = list_curated() # → ['ABS_ANNUAL_ERP_ASGS2021', 'ANA_AGG', 'AWE', 'BA_GCCSA', 'CPI', # 'ERP_Q', 'JV', 'LEND_HOUSING', 'LF', 'WPI']

When to use: - You want to know which dataflows have plain-English support - You're enumerating capabilities programmatically (e.g. building a UI) - You're showing users a "supported topics" list

Returns: Sorted list of dataflow IDs. Always 10 entries today.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Despite no annotations, the description fully discloses that only 10 curated dataflows are returned, always 10 entries, sorted, and that other dataflows are still accessible via get_data with raw codes.

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?

Well-structured with bullet points, example, and usage guidelines, though slightly verbose.

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?

Fully covers purpose, usage, behavioral details, and return format for this simple tool with an implicit output schema.

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?

Baseline 4 for 0 parameters; no additional parameter information needed.

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 lists the 10 ABS dataflow IDs with hand-curated plain-English support, distinct from siblings like describe_dataset and 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?

Provides explicit use cases (e.g., when to know which dataflows have plain-English support, building UIs, enumerating capabilities) and indirectly distinguishes from other tools, though it could more clearly state when not to use it.

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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