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Bigred97

Australian Prudential Regulation Authority

get_data

Query APRA banking, superannuation, and insurance data with filters for institution, sector, measures, and time periods. Retrieve observations in records, series, or CSV format.

Instructions

Query a curated APRA dataset and return observations.

Examples: # CBA's CET1 ratio over time resp = await get_data( "ADI_KEY_STATS", filters={"institution": "cba"}, measures="cet1_ratio", )

# Major banks' total capital, last 5 quarters
resp = await get_data(
    "ADI_KEY_STATS",
    filters={"sector": "major_banks"},
    measures="total_capital",
    start_period="2024-01-01",
)

# Total industry gross written premium (general insurance)
resp = await get_data(
    "INSURANCE_GENERAL",
    filters={"data_item": "Gross written premium",
             "industry_segment": "total_industry"},
)

# AustralianSuper member account counts
resp = await get_data(
    "SUPER_FUND_LEVEL",
    filters={"fund_name": "australian_super"},
    measures=["total_member_accounts", "total_members_benefits"],
)

Returns: DataResponse with records (or csv), unit, period bounds, row_count, source URL, the actual download_url used, optional framework info (insurance only), and CC-BY 3.0 AU attribution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoResponse shape. 'records' (default): flat list of observations. 'series': grouped by measure. 'csv': pandas CSV string in `csv` field.records
filtersNoDimension filters. Keys are plain-English aliases from the dataset's describe_dataset response. Values are matched against the source data; pass a list to OR across values. Permissive dimensions (e.g. institution, fund_name, data_item) accept any string — including substring search via trailing '*' (e.g. {'institution': 'macquarie*'}).
measuresNoWhich measure(s) to return. Plain-English keys from describe_dataset. Omit to return all measures. For long-format datasets (insurance), the single measure is 'value' and the semantic metric lives in the 'data_item' dimension filter.
dataset_idYesCurated dataset ID. Use the search or list-curated endpoint/tool to discover.
end_periodNoInclusive end period. Same format as start_period.
start_periodNoInclusive start period. Format: 'YYYY-MM-DD' (e.g. '2024-01-01'), 'YYYY-Qx' (e.g. '2024-Q1'), or 'YYYY'. Matched against the dataset's period_column (quarter-end date). Bare int years like 2024 are coerced to '2024' automatically.

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
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 full transparency burden. It states it returns observations and lists return fields but does not disclose read-only behavior, rate limits, or error conditions. While informative on output, it lacks explicit behavioral traits beyond what is obvious.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear purpose statement, but the inclusion of four example blocks and a returns list makes it longer than necessary. While examples are helpful, the description could be more concise to improve readability.

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 tool's complexity (6 parameters, output schema present), the description provides examples and lists return fields, which is nearly complete. It does not mention pagination or limits, but the return structure is adequately described. Overall, it is well-rounded for a data retrieval tool.

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%, so the baseline is 3. The description adds examples that illustrate parameter usage, but the schema already contains detailed descriptions and examples. The additional value is marginal; the description does not significantly clarify parameter semantics 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?

The description clearly states the tool queries a curated APRA dataset and returns observations, using a specific verb ('Query') and resource ('APRA dataset'). This distinguishes it from siblings like search_datasets (discovery) and describe_dataset (schema).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

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

The description provides multiple examples showing common use cases but does not explicitly state when to use this tool versus alternatives. Sibling names imply differentiation, but no direct 'when-not' or alternative references are given.

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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