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Microburbs Australian Property Data

by au.com.microburbs
OverviewDiscussionsAdmin

Suburb · Age-bracket breakdown

suburbs_demographics_age
Read-onlyIdempotent

Population by age bracket and gender, as fractions (0–1) of the suburb's residents.

Price: 3¢ per call.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "suburb_name": "<suburb_name>"
    },
    "name": "suburbs_demographics_age"
  }
}

Input Schema

NameRequiredDescriptionDefault
suburb_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "available": {
-      "anyOf": [
-        {
-          "type": "boolean"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "description": "`false` on no-data responses. Omitted on success — branch on `data !== null` if you want a single discriminator.",
-      "title": "Available"
-    },
-    "data": {
-      "anyOf": [
-        {
-          "items": {
-            "additionalProperties": true,
-            "description": "One row of demographic data — age bracket or income bracket. The\nshape varies per endpoint; common keys are always present, the rest\nare open (``extra=allow``) so a renamed sub-key doesn't break\nclients.",
-            "example": {
-              "age_bracket": "0-4",
-              "area_level": "suburb",
-              "area_name": "Belmont North",
-              "gender": "females",
-              "proportion": 0.0272
-            },
-            "properties": {
-              "age_bracket": {
-                "anyOf": [
-                  {
-                    "type": "string"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "description": "Only on /demographics/age.",
-                "title": "Age Bracket"
-              },
-              "area_level": {
-                "description": "Always 'suburb'.",
-                "title": "Area Level",
-                "type": "string"
-              },
-              "area_name": {
-                "description": "Suburb (SAL) name.",
-                "title": "Area Name",
-                "type": "string"
-              },
-              "gender": {
-                "anyOf": [
-                  {
-                    "type": "string"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "description": "Only on /demographics/age — 'males' / 'females' / 'persons'.",
-                "title": "Gender"
-              },
-              "income_bracket": {
-                "anyOf": [
-                  {
-                    "type": "string"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "description": "Only on /demographics/income.",
-                "title": "Income Bracket"
-              },
-              "median_income": {
-                "anyOf": [
-                  {
-                    "type": "integer"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "description": "Only on /demographics/income.",
-                "title": "Median Income"
-              },
-              "proportion": {
-                "anyOf": [
-                  {
-                    "type": "number"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "description": "Share of the suburb in this bracket (0–1).",
-                "title": "Proportion"
-              }
-            },
-            "required": [
-              "area_name",
-              "area_level"
-            ],
-            "title": "DemographicRow",
-            "type": "object"
-          },
-          "type": "array"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "description": "The endpoint's payload, or `null` when Microburbs has no value.",
-      "title": "Data"
-    },
-    "message": {
-      "anyOf": [
-        {
-          "type": "string"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "description": "Human-readable explanation. Omitted on success.",
-      "title": "Message"
-    },
-    "reason": {
-      "anyOf": [
-        {
-          "type": "string"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "description": "Machine-readable slug naming the no-data condition (e.g. `no_avm_for_GANSW704074813`). Stable per endpoint. Omitted on success.",
-      "title": "Reason"
-    }
-  },
-  "title": "ApiResponse[list[DemographicRow]]",
-  "type": "object",
-  "x-fastmcp-top-level-schema": "ApiResponse_list_DemographicRow__"
-}New value: +null
  2. First observed

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real value beyond that: the per-call price of 3¢ and the fact that values are fractions (0–1) rather than counts. It does not say what happens for an unknown or ambiguous suburb name, which is the main remaining behavioral gap.

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?

Two sentences, both earning their place: the first defines the measurement and its normalization, the second discloses cost. Front-loaded with the substantive content and free of filler.

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?

For a simple single-parameter lookup with no output schema, the description conveys the return shape (age bracket × gender, fractional) well enough to interpret results without further documentation. The one omission is input-format expectations for suburb_name, which matters for a string-keyed lookup.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for the single required parameter, so the description carries the full burden and does not meet it. It never explains what form suburb_name takes, whether it must match exactly, or whether state disambiguation is needed for duplicate suburb names. The only parameter is thus undocumented everywhere.

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?

States a precise verb-and-resource: population broken down by age bracket and gender, normalized as fractions of the suburb's residents. The normalization rule makes the output meaning unambiguous. It does not differentiate itself from siblings such as suburbs_demographics_all or suburbs_demographics_cohorts, which plausibly overlap.

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?

There is no when-to-use guidance, no stated preconditions, and no pointer to an alternative for a broader profile (suburbs_demographics_all) or a related cohort view. The agent must infer selection purely from the tool name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Microburbs Australian Property Data toolsInspect
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