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.

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.

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.

Return the most recent observation per measure for a dataset.

Trims to the single latest period per measure across the filtered slice — useful for "what's CBA's current CET1?" style questions without having to think about start_period.

Examples: # Latest CBA capital ratios resp = await latest("ADI_KEY_STATS", filters={"institution": "cba"})

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.

List every curated dataset ID in this version of apra-mcp.

Returns: Sorted list of dataset IDs.