AusEcon MCP for ABS | RBA | APRA data

Get Economic Series

get_economic_series

Read-onlyIdempotent

Fetch ABS, RBA, and APRA economic series using curated concepts. Filter by variant, frequency, geography, and date range for precise retrieval.

Instructions

Preferred analyst-facing retrieval tool for curated ABS/RBA economic concepts.

Use list_economic_concepts for discovery. Date bounds accept YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD and are normalised to the resolved source.

Input Schema

TableJSON Schema

Name	Required	Description
`end`	No	Optional analyst-friendly date bound: YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD. Semantic retrieval normalises this to the resolved source frequency.
`start`	No	Optional analyst-friendly date bound: YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD. Semantic retrieval normalises this to the resolved source frequency.
`last_n`	No	Optional limit returning only the most recent N observations per series; metadata.truncated is true when older observations were dropped.
`concept`	Yes	Curated semantic concept name.
`variant`	No	Optional curated concept variant, such as headline, underlying, or target.
`frequency`	No	Optional requested frequency for a curated concept, such as monthly, quarterly, or annual.
`geography`	No	Optional geography selector for a curated concept, usually aus for Australia.
`include_observation_dimensions`	No	Whether 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

Name	Required	Description
`series`	Yes	Series descriptors keyed by series_id.
`metadata`	Yes	Source, provenance, cache, and retrieval metadata for this response.
`observations`	Yes	Long-form observations keyed by date and series_id.

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedv1.11.0Jun 14, 2026

addedInput schema / properties / include_observation_dimensions

Added value: +{
+  "default": false,
+  "description": "Whether 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.",
+  "type": "boolean"
+}

addedOutput schema / properties / metadata / properties / derived / properties / alignment_method

Added value: +{
+  "enum": [
+    "locf",
+    "exact_month",
+    "period_intersection",
+    "year_ended_lag"
+  ],
+  "type": "string"
+}

addedOutput schema / properties / metadata / properties / derived / properties / description
```
Added value: +{
+  "type": "string"
+}
```

changedOutput schema / properties / metadata / properties / derived / required

Previous value: -[
-  "concept",
-  "formula",
-  "operands",
-  "source_concepts",
-  "alignment_frequency",
-  "units",
-  "requested_bounds",
-  "resolved_bounds",
-  "dropped_observations"
-]New value: +[
+  "concept",
+  "description",
+  "formula",
+  "operands",
+  "source_concepts",
+  "alignment_frequency",
+  "alignment_method",
+  "units",
+  "requested_bounds",
+  "resolved_bounds",
+  "dropped_observations"
+]

changedOutput schema / properties / observations / items / required

Previous value: -[
-  "date",
-  "series_id",
-  "value",
-  "dimensions"
-]New value: +[
+  "date",
+  "series_id",
+  "value"
+]

Changed2 schema fields changedv1.8.0Jun 10, 2026

changedInput schema / properties / last_n / description

Previous value: -"Optional positive observation count limit."New value: +"Optional limit returning only the most recent N observations per series; metadata.truncated is true when older observations were dropped."

addedOutput schema / properties / metadata / properties / observations_dropped

Added value: +{
+  "description": "Number of observations removed by last_n selection; truncated is true when this is non-zero.",
+  "minimum": 0,
+  "type": "integer"
+}

Changed4 schema fields changedv1.7.1Jun 9, 2026

addedOutput schema / properties / metadata / properties / apra_url_resolution

Added value: +{
+  "additionalProperties": false,
+  "description": "APRA workbook URL resolution strategy used for this retrieval.",
+  "properties": {
+    "seed_checked_at": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "strategy": {
+      "enum": [
+        "landing_page",
+        "seed_manifest",
+        "catalogue_fallback"
+      ],
+      "type": "string"
+    }
+  },
+  "required": [
+    "strategy",
+    "seed_checked_at"
+  ],
+  "type": "object"
+}

addedOutput schema / properties / metadata / properties / framework_breaks

Added value: +{
+  "description": "Known source framework or reporting-definition breaks relevant to the payload.",
+  "items": {
+    "additionalProperties": false,
+    "properties": {
+      "date": {
+        "type": "string"
+      },
+      "description": {
+        "type": "string"
+      },
+      "label": {
+        "type": "string"
+      }
+    },
+    "required": [
+      "date",
+      "label",
+      "description"
+    ],
+    "type": "object"
+  },
+  "type": "array"
+}

addedOutput schema / properties / metadata / properties / selection

Added value: +{
+  "description": "Observation-selection provenance when returned by convenience tools.",
+  "oneOf": [
+    {
+      "additionalProperties": false,
+      "properties": {
+        "direction": {
+          "enum": [
+            "highest",
+            "lowest"
+          ],
+          "type": "string"
+        },
+        "dropped_non_numeric_count": {
+          "minimum": 0,
+          "type": "integer"
+        },
+        "n": {
+          "minimum": 1,
+          "type": "integer"
+        },
+        "numeric_observation_count": {
+          "minimum": 0,
+          "type": "integer"
+        },
+        "returned_observation_count": {
+          "minimum": 0,
+          "type": "integer"
+        },
+        "type": {
+          "enum": [
+            "top_n"
+          ],
+          "type": "string"
+        }
+      },
+      "required": [
+        "type",
+        "n",
+        "direction",
+        "numeric_observation_count",
+        "returned_observation_count",
+        "dropped_non_numeric_count"
+      ],
+      "type": "object"
+    },
+    {
+      "additionalProperties": false,
+      "properties": {
+        "n": {
+          "minimum": 1,
+          "type": "integer"
+        },
+        "returned_observation_count": {
+          "minimum": 0,
+          "type": "integer"
+        },
+        "series_count": {
+          "minimum": 0,
+          "type": "integer"
+        },
+        "type": {
+          "enum": [
+            "latest"
+          ],
+          "type": "string"
+        }
+      },
+      "required": [
+        "type",
+        "n",
+        "series_count",
+        "returned_observation_count"
+      ],
+      "type": "object"
+    }
+  ]
+}

addedOutput schema / properties / metadata / properties / warnings

Added value: +{
+  "description": "Plain-English source warnings relevant to the payload.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

First observedv0.1.0May 31, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds useful behavioral context about date normalization ('normalised to the resolved source') and emphasizes it is analyst-facing, which helps set expectations beyond annotation hints.

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 two short paragraphs, front-loaded with purpose, then a discovery pointer, then the key date format detail. Every sentence adds value and there is no redundant filler.

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?

Given the presence of a rich output schema, thorough parameter descriptions, and comprehensive annotations, the description is complete. It covers the primary use case, directs to discovery, and highlights the most critical behavioral nuance (date normalization). No significant gaps remain for an analyst-facing retrieval tool.

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 coverage is 100% with detailed parameter descriptions (e.g., date formats, last_n truncation semantics). The description repeats some of this information (date format) but does not add meaning beyond what the schema already provides. Baseline 3 is appropriate.

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 identifies the tool as the 'Preferred analyst-facing retrieval tool for curated ABS/RBA economic concepts', specifying both the action (retrieval) and the resource (curated concepts). It distinguishes itself from siblings by positioning it as preferred and referencing discovery via list_economic_concepts.

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

Usage Guidelines4/5

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

It explicitly directs users to 'Use list_economic_concepts for discovery', providing an alternative for a related task. The phrase 'Preferred analyst-facing retrieval tool' implies when to choose this tool over others, though it doesn't detail when not to use tools like get_derived_series or get_abs_data.

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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