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Bigred97

Australian Prudential Regulation Authority

search_datasets

Fuzzy-search the APRA dataset catalog to find curated datasets on bank capital, superannuation, and insurance.

Instructions

Fuzzy-search the curated APRA dataset catalog.

All datasets ship hand-curated in v0.1: per-bank capital ratios, per-bank risk-weighted assets, fund-by-fund superannuation, and post-AASB17 life + general insurance (with separate historical archives for the pre-Q3-2023 reporting framework).

Examples: # Find the dataset for bank capital ratios results = await search_datasets("bank capital cet1") # → [{id: 'ADI_KEY_STATS', name: 'ADI Key Statistics — entity-level...', ...}]

# Discover what's available on insurance
results = await search_datasets("insurance premium")

Returns: List of DatasetSummary (id, name, description, update_frequency, is_curated), ranked by relevance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesFree-text search query. Matches against dataset IDs, names, descriptions, and curated search keywords. Case-insensitive.
limitNoMaximum number of results to return, ranked by relevance.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations exist, so description carries full burden. It reveals fuzzy-search behavior, ranking by relevance, and return structure (DatasetSummary). No contradictions.

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: leading sentence, catalog content, examples, return info. Slightly verbose but each part adds value, earning its place.

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?

Given moderate complexity, schema covers all parameters, output schema exists (not shown but noted), and description explains return type. No gaps remain.

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% with clear descriptions and examples for both parameters. The description adds value by detailing search scope (IDs, names, descriptions, keywords) and ranking.

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 'Fuzzy-search the curated APRA dataset catalog,' with a specific verb and resource. It distinguishes from siblings like 'list_curated' and 'describe_dataset' by focusing on search.

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 includes concrete examples and explains the return format, providing good usage guidance. It could explicitly mention when not to use, but examples effectively imply scenarios.

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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