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Bigred97

Reserve Bank of Australia

describe_table

Identify available series in an RBA F-table before fetching data. Returns plain-English keys for curated tables or raw series IDs for others.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_idYesRBA F-table ID like 'F1.1', 'F11', 'F6'. Use search_tables() to discover or list_curated() to enumerate the 15 plain-English tables. Case-insensitive ('f11' resolves to 'F11').

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
nameYes
seriesYes
rba_urlYes
frequencyNo
is_curatedYes
source_urlYes
descriptionYes
Behavior5/5

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

The description fully discloses behavior: it returns different outputs for curated vs raw tables, mentions fetching CSV for raw tables, explains case-insensitivity, and provides example outputs. Since no annotations are present, the description carries the full burden and meets it completely.

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 headers ('Examples:', 'When to use:', 'Returns:') and front-loaded purpose. It is slightly lengthy but every sentence adds value, including examples and usage guidance. A minor reduction for not being more compact.

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 is complete for an agent: it explains the two modes, when to use, parameter details, and return structure (summarized even though an output schema exists). Examples illustrate expected output. No gaps are apparent for this moderately complex tool.

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?

The schema covers the parameter with examples and case-insensitivity info (100% coverage). The description adds context beyond the schema by explaining how the parameter affects behavior (curated vs raw), but does not introduce new technical details. Given high schema coverage, the additional value is modest, justifying a 4.

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's purpose: 'Describe an RBA F-table's series, units, and frequency.' It distinguishes between curated tables (plain-English keys) and raw tables (RBA series IDs) using specific examples. This directly addresses what the tool does and differentiates it from siblings like get_data or list_curated.

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

Usage Guidelines5/5

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

The 'When to use:' section explicitly lists three scenarios: before calling get_data, to discover valid series keys, and to distinguish curated vs raw tables. It also implies when not to use it (e.g., to get actual data) by naming get_data and search_tables as alternatives. This provides clear guidance.

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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