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describe_dataset

List filterable dimensions, available measures, units, and source URL for a dataset to validate queries before using get_data.

Instructions

Describe a dataset's filterable dimensions, returnable measures, units, and source.

Use this before calling get_data on a new dataset — it tells you the valid filter keys ('employer_name', 'anzsic_division', 'gender', ...), enumerated filter values where they exist (e.g. 'women' → 'Women'), measure aliases ('n_employees'), and the canonical source URL.

Returns: DatasetDetail with id, name, description, period_coverage, list of dimensions, list of measures, source_url, and the resolved reporting year label.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesCurated dataset ID. Use the search endpoint or search tool to discover, or the list-curated endpoint/tool to enumerate. Case-insensitive.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
nameYes
measuresNo
dimensionsNo
is_curatedYes
source_urlYes
descriptionYes
download_urlNo
period_coverageNo
update_frequencyNo
reporting_year_latestNo
Behavior4/5

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

No annotations exist, so description carries full burden. It implicitly discloses read-only behavior by stating it describes and returns metadata, but does not explicitly confirm no side effects. The return structure is detailed, adding transparency.

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?

Concise two-paragraph structure with front-loaded purpose and immediate usage guidance. Every sentence adds value; no wasted words.

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?

Complete description for a simple tool. Covers purpose, usage, return structure, and links to sibling tools. Output schema exists, so detailed field descriptions are not needed in the description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with one parameter (dataset_id) documented with examples and description. Description adds usage context but no additional semantic detail for the parameter beyond the 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 it describes a dataset's filterable dimensions, measures, units, and source. Includes concrete examples of filter keys and measure aliases, distinguishing 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 Guidelines5/5

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

Explicitly instructs to use before calling get_data on a new dataset, providing clear when-to-use context and indicating it's a preparatory step.

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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