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Bigred97

Australian Prudential Regulation Authority

describe_dataset

Describe a dataset's dimensions, filters, measures, source URL, and insurance framework to guide data queries.

Instructions

Describe a dataset's filterable dimensions, returnable measures, units, source, and (for insurance) framework break info.

Use this before calling get_data on a new dataset — it tells you the valid filter keys ('institution', 'sector', 'data_item'), the valid enumerated filter values ('cba', 'major_banks'), the measure aliases ('cet1_ratio', 'total_capital'), and the canonical source URL.

For insurance datasets, the response includes a framework block documenting the Q3-2023 AASB-17 break.

Returns: DatasetDetail with id, name, description, period_coverage, list of dimensions, list of measures, source_url, download_url, and optional framework info.

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
frameworkNo
dimensionsNo
is_curatedYes
source_urlYes
descriptionYes
download_urlNo
period_coverageNo
update_frequencyNo
Behavior3/5

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

No annotations are provided, so the description must bear the burden. It states the tool returns a DatasetDetail with specific fields, implying a read-only operation. However, it does not mention authentication, rate limits, or safety guarantees, which would enhance transparency.

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?

The description is well-structured with paragraphs and bullet points, effectively front-loading the purpose. It is concise with no redundant information, though slightly verbose in listing return fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the single parameter and presence of an output schema, the description sufficiently explains the tool's purpose, usage, and return structure. It covers the key aspects for an agent to correctly invoke it.

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 dataset_id having a description and examples. The description adds context (case-insensitive, curated) but does not significantly augment the schema beyond that. Baseline score of 3 is appropriate.

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 the tool describes a dataset's filterable dimensions, measures, units, source, and framework break info. It uses specific verbs and resources, distinguishing it from siblings like get_data and search_datasets.

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?

The description explicitly advises using this tool before get_data on a new dataset, detailing the information it provides. While it does not list explicit exclusions or alternatives, the guidance is clear and contextually sufficient.

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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