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Bigred97

Australian Bureau of Statistics

describe_dataset

Discover dataset dimensions, value codes, and source URL before querying data. For curated datasets, get plain-English names; otherwise, get raw SDMX codes.

Instructions

Describe an ABS dataflow's filter dimensions, value codes, and source.

For curated dataflows (LF, CPI, ANA_AGG, AWE, BA_GCCSA, ERP_Q, JV, LEND_HOUSING, WPI, ABS_ANNUAL_ERP_ASGS2021), returns plain-English dimension names + curated value keys + the ABS source URL.

For other dataflows (~1,200 in total), returns raw SDMX dimensions and codelists translated to the same response shape — pass raw SDMX codes to get_data when querying these.

Examples: # Curated path — plain-English values detail = await describe_dataset("LF") # detail.dimensions = [{'name': 'region', 'values': [{'key': 'nsw', # 'sdmx_code': '1'}, {'key': 'vic', 'sdmx_code': '2'}, ...]}, ...]

# Raw path — full SDMX codelist
detail = await describe_dataset("ALC")  # Apparent Consumption of Alcohol
# detail.is_curated == False; values are raw SDMX codes

When to use: - Before calling get_data on an unfamiliar dataflow — to discover valid filter dim names and value keys - To get the canonical source URL on the ABS site - To see whether a dataflow is curated (plain-English) or raw SDMX

Returns: DatasetDetail with id, name, description, is_curated flag, the list of filter dimensions (name, sdmx_id, values), and abs_url.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesABS dataflow ID. Use search_datasets to discover, or list_curated to enumerate the 10 dataflows with plain-English support. Case-insensitive — 'lf', 'LF', and ' LF ' all resolve to 'LF'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
nameYes
descriptionYes
is_curatedYes
dimensionsYes
hidden_defaultsNo
abs_urlYes
Behavior4/5

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

Describes two distinct behaviors for curated vs. raw dataflows with examples. Notes case-insensitivity and curates visible. No annotation provided, so description carries full burden; it is thorough but could mention idempotency or performance.

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 clear sections (description, examples, when to use, returns). Slightly verbose but each sentence adds value; front-loaded with main action. Could trim redundant phrasing.

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?

Covers all necessary aspects: input parameter sourcing, two output modes, examples, and integration with sibling tools. Output schema exists, so return value details are handled. Complete for its 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?

Input schema already covers dataset_id with description and examples (100% coverage). Tool description augments by explaining how to obtain valid IDs from sibling tools and reinforcing case-insensitivity, adding practical context 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?

Clearly states 'Describe an ABS dataflow's filter dimensions, value codes, and source' with specific verb and resource. Distinguishes from siblings like get_data, list_curated, and search_datasets by explaining its role in preparation for data retrieval.

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?

Explicit 'When to use' section lists three scenarios: before calling get_data, to get source URL, and to check curation status. Also references sibling tools for obtaining dataset_id, providing clear alternatives.

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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