Fuzzy-search RBA F-tables by name and topic.

Use this when you don't know the exact table ID. The 5 curated F-tables (F1.1, F4, F6, F11, F11.1) cover the most-asked indicators: cash rate, money-market rates, household lending rates, FX rates.

Examples: # Find the F-table that publishes the cash rate results = await search_tables("cash rate") # → [{id: 'F1.1', name: 'Interest Rates and Yields - Money Market', ...}]

# Discover what's available on FX
results = await search_tables("aud usd", limit=5)
# → top 5 FX-related tables, curated F11/F11.1 first

When to use: - You have a natural-language question and need to identify the table - You want to discover what RBA publishes on a topic - You're enumerating the F-table catalog programmatically

Returns: List of TableSummary (id, name, frequency, description), ranked by relevance. Curated tables surface above the rest.

Describe an RBA F-table's series, units, and frequency.

For curated F-tables (F1.1, F4, F6, F11, F11.1), returns plain-English series keys (like 'cash_rate_target', 'aud_usd') with descriptions and units. For other F-tables, fetches the CSV and returns the raw RBA series IDs from the header along with start dates.

Examples: # Curated table — plain-English keys detail = await describe_table("F1.1") # detail.series[0]: key='cash_rate_target', series_id='FIRMMCRT', # unit='Per cent per annum', frequency='Daily'

# Curated FX table
detail = await describe_table("F11.1")
# detail.series has 'aud_usd', 'aud_eur', 'aud_jpy', 'aud_cny', etc.

When to use: - Before calling get_data on a new table — to discover valid series keys - To get the canonical RBA source URL for citation - To distinguish curated (plain-English) tables from raw F-tables

Returns: TableDetail with id, name, description, is_curated flag, frequency, list of SeriesDetail (key, series_id, description, unit), and rba_url.

Query an RBA F-table and return observations.

Curated tables accept plain-English series keys that map to canonical
RBA series IDs server-side. Omit `series` to get the table's headline
series (e.g. F1.1 → cash rate target, F11/F11.1 → AUD/USD, F6 → owner-
occupier outstanding variable rate). Pass an explicit list for a multi-
series query.

Examples:
    # Cash rate target since 2020 (portfolio-standard name)
    resp = await get_data("F1.1", series="cash_rate_target", start_period="2020")
    # → resp.records[0]: period='2020-01-01', value=0.25, series='cash_rate_target'

    # Headline default — no series arg returns the table's canonical series
    resp = await get_data("F11.1", start_period="2024-01-01", end_period="2024-12-31")
    # → resp.records: AUD/USD daily (the headline) for the period

    # Multiple FX rates — pass an explicit list
    resp = await get_data(
        "F11.1",
        series=["aud_usd", "aud_eur", "aud_jpy"],
        start_period="2024-01-01",
        end_period="2024-12-31",
    )

    # Mortgage rates as CSV
    resp = await get_data("F6", format="csv", start_period="2023")
    # → resp.csv = "date,series,value

2023-01-01,..."

    # Raw (non-curated) F-table — pass raw RBA series IDs
    resp = await get_data("F1", series=["FIRMMCRTD", "FIRMMBAB30"])

    # Legacy alias still works (start_date / end_date)
    resp = await get_data("F11", series="aud_usd", start_date="2024")

Parameter notes:
    - Prefer `start_period` / `end_period` (portfolio-standard names; 7
      of 9 sister MCPs use them).
    - `start_date` / `end_date` are retained as legacy aliases.
      Supplying both `start_period` and `start_date` (or `end_period`
      and `end_date`) raises ValueError — pick one per pair.

When to use:
    - You want a time series of an RBA indicator (use latest() for current-only)
    - You want a multi-series comparison (e.g. all FX rates)
    - You want CSV for downstream charting

Returns:
    DataResponse with records, unit, period bounds, RBA source URL,
    and CC-BY 4.0 attribution.

Return the most recent observation for each series in an RBA F-table.

Wraps get_data with last_n=1 (and a shorter cache TTL). Use this for "what's the current X?" questions — it's a cheap, fast call.

Examples: # Current cash rate target (explicit) resp = await latest("F1.1", series="cash_rate_target") # → resp.records[0]: period='2026-05-06', value=3.85, unit='Per cent per annum'

# Headline default — no series arg returns the table's canonical series.
# F1.1 → cash rate target; F11/F11.1 → AUD/USD; F6 → average mortgage rate.
resp = await latest("F1.1")
# → resp.records[0]: cash_rate_target only (the table's headline)

# Snapshot multiple FX rates in one call
resp = await latest("F11.1", series=["aud_usd", "aud_eur", "aud_jpy"])

# Latest owner-occupier variable mortgage rate
resp = await latest("F6", series="owner_occupier_variable_existing")

When to use: - You want the current value of an RBA indicator - You want a current-snapshot of multiple series in one call (pass an explicit list — e.g. all FX rates) - You want sub-50ms warm-cache latency for chat integration

Returns: DataResponse with one most-recent observation per requested series.

List the 5 RBA F-table IDs with hand-curated plain-English support.

These are the tables where get_data and latest accept plain-English series keys (like 'cash_rate_target', 'aud_usd'). Other F-tables are still queryable via raw RBA series IDs.

The 5 curated F-tables: - F1.1 — Interest Rates and Yields: Money Market (incl. cash rate target) - F4 — Money Market Operations - F6 — Housing Lending Rates (standard variable, fixed, etc.) - F11 — Exchange Rates (AUD vs major currencies, daily) - F11.1 — Exchange Rate Indices (TWI, real TWI)

Example: ids = list_curated() # → ['F1.1', 'F11', 'F11.1', 'F4', 'F6']

When to use: - You want to know which tables have plain-English support - You're building a UI / agent that needs the supported set up front - You want to plan which F-tables to call without inspecting each

Returns: Sorted list of F-table IDs. Always 5 entries today.

Upcoming RBA publication schedule (data + statements + events).

Scrapes https://www.rba.gov.au/schedules-events/ and merges the two schedule tables into a single chronological feed. Each entry reports release_at (Sydney local with UTC offset), title, event_type, dataset_id (curated F-table key when the release refreshes one, else null), publication_id, and source_url.

Event types: - data_release — regular statistical publication (Financial Aggregates, Retail Payments, Index of Commodity Prices, etc.) - statement — narrative release (Statement on Monetary Policy, Minutes of Monetary Policy Meeting, Financial Stability Review, Bulletin, Chart Pack) - policy_decision — cash-rate decisions are NOT exposed here; they appear on a separate RBA page. The Statement on Monetary Policy and Minutes that follow ~24h and ~2 weeks later DO appear, tagged as statement.

Returns the same envelope shape as abs-mcp.release_calendar so a gateway poller can dispatch both feeds through the same code path.

Cached at 24h TTL with stale-fallback on 5xx (per portfolio graceful-degradation policy). The gateway should poll on its own schedule rather than hitting the live HTML.

Examples: cal = await release_calendar(7) for r in cal.releases: print(r.release_at, r.event_type, r.title, r.publication_id)

cal = await release_calendar(60)
statements = [r for r in cal.releases if r.event_type == "statement"]