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AnthonyPuggs

AusEcon MCP for ABS | RBA | APRA data

Get ABS Data

get_abs_data
Read-onlyIdempotent

Retrieve normalized ABS time-series data using SDMX keys, with optional period bounds, last N observations, and updated-after filtering.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataflow_idYesNon-empty dataset or table id.
keyNoABS SDMX key, or "all" for all series.all
start_periodNoOptional ABS period bound in YYYY, YYYY-QN, YYYY-MM, or YYYY-SN format.
end_periodNoOptional ABS period bound in YYYY, YYYY-QN, YYYY-MM, or YYYY-SN format.
last_nNoOptional limit returning only the most recent N observations per series; metadata.truncated is true when older observations were dropped.
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
metadataYesSource, provenance, cache, and retrieval metadata for this response.
seriesYesSeries descriptors keyed by series_id.
observationsYesLong-form observations keyed by date and series_id.
Behavior3/5

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

Annotations already provide readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds 'normalised response shape' but does not disclose behavioral traits like pagination, rate limits, or error handling beyond what annotations offer.

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 short sentence, which is concise. However, the phrase 'expert/source-native' may be ambiguous and lacks clarity for an agent.

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?

Given the tool has 7 parameters and an output schema, the description is minimal. It does not explain how parameters interact or provide usage context. Adequate but not comprehensive.

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 schema already documents all parameters. The description does not add extra meaning to the parameters beyond what is in the 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 states it retrieves ABS data in a normalised SDMX format, clearly identifying the resource (ABS) and action (retrieval). However, it does not explicitly differentiate it from sibling tools like get_apra_data or get_rba_table, though the name itself helps.

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 on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or comparisons with sibling tools like get_economic_series or describe_dataset.

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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