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Bigred97

Reserve Bank of Australia

latest

Return the most recent observation for any RBA F-table series. Get current cash rate, AUD/USD, or mortgage rates in one fast call.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seriesNoWhich series to return. For curated tables: plain-English keys. Pass None (default) to get the table's headline series — e.g. F1.1 returns the cash rate target, F11/F11.1 returns AUD/USD. Pass an explicit list to get multiple series in one snapshot.
table_idYesRBA F-table ID. Use search_tables() to discover.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvNo
unitNo
queryNo
staleNo
periodNo
sourceNoReserve Bank of Australia
rba_urlYesClick-through URL for this table's source page. rba-mcp legacy name — prefer source_url (canonical) for new code. Both fields are populated identically.
recordsNo
table_idYes
row_countNoNumber of observation rows in records.
source_urlYesCanonical click-through URL. Same value as rba_url; both populated for backward compat.
table_nameYes
attributionNoData sourced from the Reserve Bank of Australia and licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). https://www.rba.gov.au/copyright/
retrieved_atYes
stale_reasonNo
truncated_atNo
server_versionNo
Behavior4/5

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

With no annotations, the description explains behavior well: it wraps get_data with last_n=1, uses shorter cache TTL, is cheap and fast, and returns one observation per series. It could explicitly state 'read-only' but the context is clear.

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

Conciseness4/5

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

The description is well-structured with a clear definition, examples, usage guidance, and return info. Every sentence adds value, though a few could be tightened without losing clarity.

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?

The description fully covers purpose, parameters, examples, usage scenarios, performance characteristics, and return format. Given the tool's simplicity and the presence of output schema, nothing essential is missing.

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

Parameters4/5

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

Schema coverage is 100%, so baseline 3. Description adds meaning by explaining the 'series' parameter's default behavior (headline series) and plain-English keys, and notes that 'table_id' can be discovered via search_tables(). Examples further clarify usage.

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 returns the most recent observation per series, using the verb 'Return' and specific resource 'RBA F-table'. It distinguishes itself from sibling 'get_data' by explicitly describing it as a wrapper with last_n=1 and shorter cache TTL.

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 includes a 'When to use:' section listing appropriate scenarios like current values and multiple series snapshots. It implies not for historical analysis, but lacks explicit 'when not to use' or direct alternatives like 'for historical data, use get_data instead'.

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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