Skip to main content
Glama

Australian Economic Data (ABS, RBA & APRA)

by io.github.AnthonyPuggs

Describe Dataset

describe_dataset
Read-onlyIdempotent

Describe a source-native ABS, RBA, or APRA dataset without hiding native IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesSource selector. Use abs for Australian Bureau of Statistics, rba for Reserve Bank of Australia, or apra for Australian Prudential Regulation Authority.
table_idNoNon-empty dataset or table id.
identifierYesNon-empty dataset or table id.
include_structureNoWhether ABS descriptions should include source-native structure details.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the behavioral trait 'without hiding native IDs', clarifying that raw identifiers are preserved in the output, which is valuable beyond the schema. It does not mention other behaviors like error handling, but with strong annotations this is acceptable.

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

Conciseness5/5

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

The description is a single sentence of 12 words, directly front-loaded with the verb and resource. Every word earns its place; no fluff or redundant information.

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 schema (100% parameter coverage), comprehensive annotations, and an output schema, the description is adequate for selecting and invoking the tool. The 'without hiding native IDs' clause is a key differentiator that aids selection among siblings, though it doesn't explicitly explain return structure—covered by the output schema.

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%, with each parameter already described in the input schema. The description itself adds no parameter-specific information beyond referencing the source categories, so it neither compensates nor hinders.

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 uses the specific verb 'Describe' with a clear resource ('source-native ABS, RBA, or APRA dataset') and adds the distinctive clause 'without hiding native IDs', which differentiates it from sibling tools like get_abs_data or get_abs_dataset_structure. This gives a precise purpose.

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

Usage Guidelines3/5

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

The description implies the tool is used for describing datasets but does not explicitly state when to prefer it over alternatives or any exclusions. Sibling tools like search_datasets or get_abs_dataset_structure are not referenced, leaving usage context implicit.

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
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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