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AnthonyPuggs

AusEcon MCP for ABS | RBA | APRA data

by AnthonyPuggs
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Get RBA Table

get_rba_table
Read-onlyIdempotent

Retrieves RBA statistical tables with optional date, series, and observation limits, returning normalised data for analysis.

Instructions

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. Changed5 schema fields changedv1.11.0
    • 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"
+]
  2. Changed2 schema fields changedv1.8.0
    • 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"
+}
  3. Changed4 schema fields changedv1.7.1
    • 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"
+}
  4. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the base transparency is high. The description adds 'normalised response shape', which offers some insight into output format but does not reveal behavioral traits like error handling or default behavior. No contradiction with annotations.

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 sentence with no wasted words. It is front-loaded but uses jargon ('Expert/source-native', 'normalised') which may reduce clarity. Could be more straightforward.

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

Completeness2/5

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

Given the tool has 6 parameters, an output schema, and is used for retrieving statistical tables, the description is too sparse. It lacks explanation of pagination, error handling, or typical use cases. The annotation-rich structure mitigates some gaps, but completeness is low.

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 descriptions for all 6 parameters. The description adds no additional meaning beyond what the schema provides (e.g., 'table_id' is described as non-empty dataset or table id). Baseline 3 is appropriate.

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 'retrieval' as the verb and specifies 'RBA statistical table' as the resource, clearly indicating the tool's function. It distinguishes from siblings like 'list_rba_tables' (list vs get) and 'get_abs_data' (different source). The phrase 'normalised response shape' hints at output structure, but the purpose is clear.

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 explicit guidance on when to use this tool versus alternatives like 'list_rba_tables' for discovery or other statistical retrieval tools. No exclusions or context are provided. The term 'Expert/source-native' implies specialized use but lacks clarity.

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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