io.ausdata/abs-mcp

Describe an ABS dataflow's filter dimensions, value codes, and source. For curated dataflows (LF, CPI, ANA_AGG, AWE, BA_GCCSA, ERP_Q, JV, LEND_HOUSING, WPI, ABS_ANNUAL_ERP_ASGS2021), returns plain-English dimension names + curated value keys + the ABS source URL. For other dataflows (~1,200 in total), returns raw SDMX dimensions and codelists translated to the same response shape — pass raw SDMX codes to get_data when querying these. Examples: # Curated path — plain-English values detail = await describe_dataset("LF") # detail.dimensions = [{'name': 'region', 'values': [{'key': 'nsw', # 'sdmx_code': '1'}, {'key': 'vic', 'sdmx_code': '2'}, ...]}, ...] # Raw path — full SDMX codelist detail = await describe_dataset("ALC") # Apparent Consumption of Alcohol # detail.is_curated == False; values are raw SDMX codes When to use: - Before calling get_data on an unfamiliar dataflow — to discover valid filter dim names and value keys - To get the canonical source URL on the ABS site - To see whether a dataflow is curated (plain-English) or raw SDMX Returns: DatasetDetail with id, name, description, is_curated flag, the list of filter dimensions (name, sdmx_id, values), and abs_url.

Query an ABS dataflow and return observations. Pass filters and/or a period range — unfiltered queries on large dataflows can return tens of thousands of observations. Curated dataflows accept plain-English filter keys and values that are translated to SDMX codes server-side. For example, on LF: `{"region": "nsw", "measure": "unemployment_rate"}` resolves to SDMX key `M13.3.1599.20.1.M` with hidden-dim defaults auto-applied. Examples: # NSW unemployment monthly for 2024 resp = await get_data( "LF", filters={"region": "nsw", "measure": "unemployment_rate"}, start_period="2024", end_period="2024-12", ) # → resp.records[0]: period='2024-01', value=4.8, unit='Percent' # Multi-state comparison resp = await get_data( "LF", filters={"region": ["nsw","vic","qld"], "measure": "unemployment_rate"}, start_period="2024", format="csv", ) # → resp.csv contains 36 rows (3 states × 12 months) # Australia quarterly CPI annual change resp = await get_data( "CPI", filters={"region": "australia", "measure": "change_year"}, start_period="2020", ) When to use: - You want observations over a time range (use latest() for the most-recent only) - You want a multi-state or multi-measure comparison via list filters - You want a CSV for downstream charting / spreadsheet tools Returns: DataResponse with records (list of {period, value, dimensions, unit}), unit (when homogeneous), period bounds, the resolved query echo, the ABS source URL, and the CC-BY 4.0 attribution string.

Return the most recent observation(s) for a dataflow. Wraps get_data with lastNObservations=1 and a 15-minute cache TTL (vs 1 hour for general data calls). Use this for "what's the current X?" questions — it's a cheap, fast call: warm-cache p50 ~22ms, cold-cache ~200ms. Examples: # Latest NSW unemployment rate resp = await latest("LF", {"region": "nsw", "measure": "unemployment_rate"}) # → resp.records[0]: period='2026-03', value=4.61, unit='Percent' # Latest Australia headline annual inflation resp = await latest("CPI", {"region": "australia", "measure": "change_year"}) # → resp.records[0]: period='2026-Q1', value=4.6, unit='Percent' # Latest Greater Sydney population resp = await latest("ABS_ANNUAL_ERP_ASGS2021", {"region": "greater_sydney", "region_type": "gccsa"}) # → resp.records[0]: period='2025', value=5640000, unit='Persons' When to use: - You want "the current value" of an indicator (most common workflow) - You're answering a "what's the unemployment rate?" style question - You want sub-50ms warm-cache latency for chat/agent integration Returns: DataResponse with one most-recent observation per matched dimension combination, capped at `limit` rows. Same envelope as get_data. `truncated_at` is set to the original row count when the cap fires.

List the 10 ABS dataflow IDs with hand-curated plain-English support. These are the dataflows where get_data accepts plain-English filter keys (`{"region": "nsw"}`) and describe_dataset returns rich human-readable metadata. All other ABS dataflows (~1,200) are still accessible via get_data with raw SDMX dimension IDs and codes. The 10 curated dataflows: - LF — Labour Force (unemployment, employment, participation) - CPI — Consumer Price Index (inflation) - WPI — Wage Price Index (wage growth) - AWE — Average Weekly Earnings - JV — Job Vacancies - BA_GCCSA — Building Approvals (by Greater Capital City) - LEND_HOUSING — Lending Indicators / Housing Finance - ANA_AGG — National Accounts (GDP) - ERP_Q — Estimated Resident Population (quarterly) - ABS_ANNUAL_ERP_ASGS2021 — Population (annual; supports SA2/SA3/SA4) Example: ids = list_curated() # → ['ABS_ANNUAL_ERP_ASGS2021', 'ANA_AGG', 'AWE', 'BA_GCCSA', 'CPI', # 'ERP_Q', 'JV', 'LEND_HOUSING', 'LF', 'WPI'] When to use: - You want to know which dataflows have plain-English support - You're enumerating capabilities programmatically (e.g. building a UI) - You're showing users a "supported topics" list Returns: Sorted list of dataflow IDs. Always 10 entries today.

Upcoming ABS publication schedule (data releases). Scrapes the official ABS release calendar (https://www.abs.gov.au/release-calendar/future-releases-calendar) and returns each scheduled publication with its release timestamp, title, reference period, and — when the title maps to a curated abs-mcp dataset — the `dataset_id` an agent can plug into `get_data` or `latest`. Curated mappings cover the 10 datasets in `list_curated()` plus a handful of commonly-watched non-curated catalogues (Retail Trade, International Trade in Goods, etc., where `dataset_id` stays null but `publication_id` carries the ABS catalogue number). `release_at` is returned with Sydney's local UTC offset (`+10:00` AEST or `+11:00` AEDT) — what ABS publishes against. The DST switch is naive (month-based), within an hour of correct at the changeover boundary; downstream code should treat the offset as authoritative rather than re-deriving local time. Examples: # Next 7 days cal = await release_calendar(7) for r in cal.releases: print(r.release_at, r.title, r.dataset_id) # Filter to curated datasets only cal = await release_calendar(30) curated_releases = [r for r in cal.releases if r.dataset_id] When to use: - Building a webhook / notification feed (ABS publishes at 11:30 AEST) - "What's next from the ABS?" agent answers - Pre-warming caches the morning of a known release Returns: `ReleaseCalendarResponse` — same envelope shape as `rba-mcp`'s `release_calendar` for portfolio interop. Sorted ascending by `release_at`. `stale=True` + `stale_reason` is set when the live HTML scrape failed and a cached payload was served past TTL.

Fuzzy-search ABS dataflow names, descriptions, and keywords. Use this when you don't know the exact dataset ID. The 10 curated dataflows (LF, CPI, ANA_AGG, etc.) get a relevance boost so common queries like "unemployment" or "gdp" return the right dataset at rank #1 — not one of ABS's 800+ census tables that mention these keywords incidentally. Examples: # Discover which dataflow answers "what's NSW unemployment?" results = await search_datasets("unemployment") # → [{id: 'LF', name: 'Labour Force', is_curated: True}, ...] # Broader topic exploration results = await search_datasets("housing", limit=5) # → top 5 housing-related dataflows, curated first When to use: - You have a natural-language question and need to identify the dataset - You want to discover what ABS publishes on a topic - You're not sure if a topic has a plain-English (curated) mapping yet Returns: List of DatasetSummary (id, name, description, is_curated), ranked by relevance. Curated dataflows surface above raw SDMX dataflows.

Return the N rows with the largest (or smallest) value of a measure. Ranks across the most-recent available period only (uses lastNObservations=1 under the hood) so the result is a clean "top N entities at the latest period" view — not noisy historical highs. This is the most common agent workflow: "show me the top 10 X by Y". Without this tool, an agent would call get_data, receive the full time series, and then sort/slice locally — wasting tokens and turns. top_n does the rank server-side and returns only the requested rows. Examples: # 5 states with the highest current unemployment rate top_n("LF", "unemployment_rate", n=5) # 10 GCCSAs with the largest populations top_n("ABS_ANNUAL_ERP_ASGS2021", "estimated_resident_population", n=10, filters={"region_type": "gccsa"}) # 3 industries with the lowest wage growth top_n("WPI", "wage_price_index", n=3, direction="bottom") Returns: DataResponse with at most `n` records, sorted by `measure` value in the requested direction. Other fields (period, unit, attribution) match a regular get_data call.