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Glama

Australian Economic Data (ABS, RBA & APRA)

by io.github.AnthonyPuggs

Get RBA Table

get_rba_table
Read-onlyIdempotent

Expert/source-native RBA statistical table retrieval in a normalised response shape.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
last_nNoOptional limit returning only the most recent N observations per series; metadata.truncated is true when older observations were dropped.
end_dateNoOptional ISO date bound in YYYY-MM-DD format.
table_idYesNon-empty dataset or table id.
series_idsNoOptional list of non-empty source-native series IDs to keep after download.
start_dateNoOptional ISO date bound in YYYY-MM-DD format.
include_observation_dimensionsNoWhether to repeat the full dimension dict on every observation. Off by default because the same dimensions already appear on each series descriptor and are encoded in series_id.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
seriesYesSeries descriptors keyed by series_id.
metadataYesSource, provenance, cache, and retrieval metadata for this response.
observationsYesLong-form observations keyed by date and series_id.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds that the response shape is 'normalised', which is a useful behavioral trait, but it does not disclose other behaviors like truncation or pagination beyond what the schema already documents.

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 a single concise sentence with no wasted words. It front-loads the purpose and does not pad with irrelevant details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the rich input schema (100% parameter descriptions), output schema, and comprehensive annotations, the description is sufficient for a read-only retrieval tool. It lacks some usage context, but the missing guidance is not critical for basic invocation correctness.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific meaning; all parameter semantics are already carried by the input schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves RBA statistical tables, with the verb 'retrieval' and resource 'RBA statistical table'. The RBA prefix distinguishes it from sibling tools like get_abs_data and get_apra_data, though the phrase 'Expert/source-native' adds a bit of ambiguity.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives such as get_abs_data or get_apra_data. It does not mention any exclusions, prerequisites, or explicit selection criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Australian Economic Data (ABS, RBA & APRA) toolsInspect
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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have clear source or function boundaries (ABS, RBA, APRA, derived series, curated concepts, discovery). However, get_derived_series and get_economic_series both retrieve time-series data and could be confused; list_catalogue and search_datasets also overlap in discovery. The descriptions help, but these pairs require careful reading.

Naming Consistency5/5

All tools follow a consistent lowercase verb_noun pattern with underscores (describe_, get_, list_, search_). Verbs are used predictably: get for retrieval, list for enumeration, search for discovery. No mixed conventions or camelCase.

Tool Count4/5

14 tools is on the higher end but appropriate for a multi-source economic data API covering ABS, RBA, and APRA. The inclusion of a deprecated alias (list_rba_tables) adds slight redundancy, but the overall count is justified by distinct operations.

Completeness4/5

The surface covers discovery (search_datasets, list_catalogue), metadata (describe_dataset, structure), retrieval (source-specific and curated), and convenience wrappers (latest/top observations). Minor gaps include lack of a unified structure tool for RBA/APRA and no bulk download option, but core read-only workflows are well covered.

Resources