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Bigred97

Australian Prudential Regulation Authority

top_n

Retrieve the top or bottom N entities by any measure from APRA banking, superannuation, or insurance datasets. Returns only the ranked rows.

Instructions

Return the N rows with the largest (or smallest) value of a measure.

The single most common agent workflow: "show me the top 10 X by Y". top_n does the rank server-side and returns only the requested rows.

Examples: # Biggest 10 banks by total capital, latest quarter top_n("ADI_KEY_STATS", "total_capital", n=10, filters={"period": "2025-12-31"})

# Most members per super fund (latest)
top_n("SUPER_FUND_LEVEL", "total_member_accounts", n=10,
      filters={"period": "2025-12-31"})

# 5 lowest CET1 ratios in the latest quarter
top_n("ADI_KEY_STATS", "cet1_ratio", n=5, direction="bottom",
      filters={"period": "2025-12-31"})

Returns: DataResponse with at most n records, sorted by measure in the requested direction. Other fields match get_data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoHow many top (or bottom) rows to return.
filtersNoOptional dimension filters, same shape as get_data. Typically you'll pin a single period to make rank meaningful (e.g. {'period': '2025-12-31'} for the latest quarter).
measureYesPlain-English measure key to rank by. Use the describe endpoint or describe tool to see available measures.
directionNo'top' returns the N rows with the LARGEST measure values (biggest bank, highest capital, largest fund). 'bottom' returns the SMALLEST.top
dataset_idYesCurated dataset ID.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvNo
unitNo
queryNo
staleNo
periodNo
sourceNoAustralian Prudential Regulation Authority
recordsNo
apra_urlYesClick-through URL for this dataset's source page. apra-mcp legacy name — prefer source_url (canonical) for new code. Both fields are populated identically.
frameworkNo
row_countNo
dataset_idYes
source_urlYesCanonical click-through URL. Same value as apra_url; both populated for backward compat.
attributionNoSource: Australian Prudential Regulation Authority. Licensed under Creative Commons Attribution 3.0 Australia (https://creativecommons.org/licenses/by/3.0/au/).
dataset_nameYes
download_urlNo
retrieved_atYes
stale_reasonNo
truncated_atNo
server_versionNo
Behavior5/5

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

With no annotations, the description fully discloses behavior: server-side ranking, limited rows, sorted by measure in requested direction, and return shape matching get_data. No contradictions or omissions.

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?

Sparse yet complete: one sentence for purpose, concise explanation, three clear examples, and one-line return note. Every sentence contributes meaning with no redundancy.

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 workflow, parameters, examples, return behavior, and even references related tools (describe). Given five parameters and output schema, this is fully self-contained guidance.

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

Parameters5/5

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

Schema coverage is 100%, but description adds significant value with practical examples, context (e.g., pinning a period), and references to describe tool. The inline examples demonstrate usage tying parameters together.

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 returns N rows with largest/smallest value of a measure, using specific verbs and resource. It distinguishes itself from siblings like get_data by focusing on ranking and limiting results server-side.

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 calls it the 'single most common agent workflow' and provides examples, implying when to use. However, it lacks explicit guidance on when not to use or alternatives, which would elevate it to a 5.

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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