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Australian Economic Data (ABS, RBA & APRA)

by io.github.AnthonyPuggs

Get ABS Data

get_abs_data
Read-onlyIdempotent

Expert/source-native ABS SDMX retrieval in a normalised response shape.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoABS SDMX key, or "all" for all series.all
last_nNoOptional limit returning only the most recent N observations per series; metadata.truncated is true when older observations were dropped.
end_periodNoOptional ABS period bound in YYYY, YYYY-QN, YYYY-MM, or YYYY-SN format.
dataflow_idYesNon-empty dataset or table id.
start_periodNoOptional ABS period bound in YYYY, YYYY-QN, YYYY-MM, or YYYY-SN format.
updated_afterNoOptional ISO date or datetime accepted by the ABS updatedAfter API.
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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safe, non-destructive nature is clear. The description adds that the response is 'normalised' and that the tool is 'source-native', which hints at output formatting and integration with ABS specifics, but it does not disclose potential rate limits, error behavior, or interactions between parameters beyond what the schema already notes.

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, making it easy to parse. However, the phrase 'Expert/source-native' is vague and arguably unnecessary, and the extreme brevity leaves out crucial usage context, meaning it sacrifices informativeness for compactness.

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

Completeness3/5

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

With a rich schema, output schema, and good annotations, the description does not need to restate those details. Yet it fails to provide the 'when to use this' context essential for a tool with many siblings, making the overall package merely adequate rather than complete.

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?

Input schema has 100% coverage, with every parameter offering a meaningful description, so the baseline is 3. The description itself adds no parameter-specific semantics; it does not clarify how 'key', 'last_n', or 'period' interact, but the schema already handles this adequately.

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 uses a specific verb ('retrieval') and resource ('ABS SDMX'), clearly indicating it fetches Australian Bureau of Statistics data via SDMX. However, it does not explicitly differentiate among the many sibling data-retrieval tools, relying on the 'ABS' qualifier to set it apart from APRA/RBA tools but not from other ABS-specific tools like get_latest_observations.

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?

The description provides no guidance on when to use this tool versus alternatives such as get_apra_data, get_latest_observations, or get_derived_series. There is no mention of prerequisites, typical scenarios, or exclusions, leaving the agent to infer usage solely from the tool name and schema.

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