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Microburbs Australian Property Data

by au.com.microburbs
OverviewDiscussionsAdmin

Count matching suburbs

suburbs_finder_count
Read-onlyIdempotent

How many suburbs match the filter, without paying per row. 5c.

Use this to size a result before searching: /search is capped at 25 rows per call, so total here tells you how many offset pages you need.

Price: 5¢ per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoRange filter `fieldKey:min..max` (repeatable). Valid fieldKeys: medianHousePrice, grossYield, medianRent, growthForecast4yr, growth10yrCagr, growth3yrCagr, rentGrowth3yr, soldAtLossPct, daysOnMarket, vacancyRate, unitMedianPrice, unitGrossYield, unitMedianRent, unitGrowthForecast4yr, unitGrowth10yrCagr, unitGrowth3yrCagr, unitRentGrowth3yr, unitSoldAtLossPct, unitDaysOnMarket, unitVacancyRate, stockOnMarket, monthsOfInventory, landValuePerSqm, lifestyleScore, safetyScore, hipScore, affluenceScore, renters, distanceCbdKm, publicHousingPct, overseasBornPct, welfareReliancePct, unemploymentPct, singleParentsPct, familyHouseholdsPct, privateSchoolPct, medianIncomeWeekly, communityDepthIndex, premiumRenovationIndex, homeOfficeIndex, tranquilityIndex, innovationEconomyIndex, alternativeLivingIndex, investorConcentration, negativeGearingExposure, mortgageStress. Apartment/unit metrics are the `unit`-prefixed keys (unitMedianPrice, unitGrossYield, unitMedianRent, …); the unprefixed market keys are houses. Unit data only exists where a suburb has a real unit market — coverage runs ~13-54% of suburbs depending on the field, so a unit filter implicitly excludes house-only suburbs.
statesNoComma-separated states (names or abbreviations), e.g. `VIC`.
regionsNoComma-separated EXACT SA4 region names — call GET /v1/suburbs/finder/regions for the list. A metro name like `Melbourne` alone will NOT match.
property_typeNoLegacy flag: 'unit' re-points the ten unprefixed house-market keys (medianHousePrice, grossYield, ...) at their unit column. It does NOT affect the explicit `unit`-prefixed keys, which are always units. Prefer the `unit` keys — they are visible in /fields and can be mixed with house keys in one query.house

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": [
-        {
-          "description": "How many suburbs match the filter — without paying for the rows.",
-          "example": {
-            "total": 126
-          },
-          "properties": {
-            "total": {
-              "description": "Suburbs matching every range filter (after the hard exclusions).",
-              "title": "Total",
-              "type": "integer"
-            }
-          },
-          "required": [
-            "total"
-          ],
-          "title": "CountResponse",
-          "type": "object"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "description": "The endpoint's payload, or `null` when Microburbs has no value."
-    },
-    "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[CountResponse]",
-  "type": "object",
-  "x-fastmcp-top-level-schema": "ApiResponse_CountResponse_"
-}New value: +null
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds genuine behavioral context beyond annotations: the 5¢ per-call pricing, the fact that it does not charge per row, and how the returned total relates to offset pagination in the sibling search tool. Minor gap: the exact response shape isn't described, but the annotations carry the safety burden.

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 short and front-loaded: it opens with the count purpose and pricing, then gives usage guidance. The only slight waste is repeating the price ('5c.' and later '**Price: 5¢ per call.**'), and the third paragraph could have been folded in more tightly, but overall it is compact and well organized.

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?

For a simple count tool with no output schema, this is complete: it tells the agent what the tool returns conceptually ('total'), when to use it, how it relates to pagination in the search sibling, and what it costs. The rich schema descriptions cover the parameters, and annotations cover safety. Nothing essential is missing.

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%, and the schema itself provides very rich parameter documentation, especially for `filter` (range format, all valid fieldKeys, house vs unit semantics, coverage caveats). The tool description itself doesn't add parameter-level meaning beyond what the schema already provides, so the high-coverage baseline of 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 states a specific verb and resource: 'How many suburbs match the filter' — a count operation on the suburbs finder. It also distinguishes itself from the row-returning sibling by noting it counts 'without paying per row,' so an agent can tell it apart from suburbs_finder_search.

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

Usage Guidelines5/5

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

The description explicitly says when to use it: 'Use this to size a result before searching,' and explains the alternative's constraint ('/search is capped at 25 rows per call') and how the result maps to pagination ('total here tells you how many offset pages you need'). This is direct, actionable routing guidance.

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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