Microburbs Australian Property Data

Crime (by suburb)

suburbs_crime_by_mesh_block

Read-onlyIdempotent

Shown in the suburb report as the 'Crime' map layer in the Risks section.

Total crime rate for every mesh block in the suburb, keyed by mesh-block code. Values are predicted incidents per 100,000 residents per year (see the payload's unit / definition keys).

Price: 20¢ 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_crime_by_mesh_block"
  }
}

Input Schema

Name	Required	Description	Default
`suburb_name`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 15, 2026

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": [
-        {
-          "additionalProperties": true,
-          "description": "Total crime rate per mesh block (choropleth-ready).",
-          "example": {
-            "area_level": "suburb",
-            "area_name": "Belmont North",
-            "definition": "Sum of the 8 modelled per-category crime rates for the mesh block (model trained on QLD Police mesh-block incidents averaged over 2020-2022, predicted for all Australian mesh blocks).",
-            "mesh_blocks": {
-              "10431160000": 12128.8,
-              "10431170000": 1755.8,
-              "10431200000": 2050.2
-            },
-            "metric": "total_predicted_crime_rate",
-            "period": "annual",
-            "unit": "predicted incidents per 100,000 residents per year"
-          },
-          "properties": {
-            "area_level": {
-              "description": "Always 'suburb' for these endpoints.",
-              "title": "Area Level",
-              "type": "string"
-            },
-            "area_name": {
-              "description": "Suburb (SAL) name.",
-              "title": "Area Name",
-              "type": "string"
-            },
-            "definition": {
-              "description": "One-sentence definition of the metric, including model provenance.",
-              "title": "Definition",
-              "type": "string"
-            },
-            "mesh_blocks": {
-              "additionalProperties": {
-                "type": "number"
-              },
-              "description": "ABS 2021 mesh-block code → total crime rate: sum of all 8 modelled crime-type rates, predicted incidents per 100,000 residents per year.",
-              "title": "Mesh Blocks",
-              "type": "object"
-            },
-            "metric": {
-              "description": "Always 'total_predicted_crime_rate' — what the mesh_blocks values measure.",
-              "title": "Metric",
-              "type": "string"
-            },
-            "period": {
-              "description": "Always 'annual' — the rates are yearly figures.",
-              "title": "Period",
-              "type": "string"
-            },
-            "unit": {
-              "description": "Unit of the mesh_blocks values — always 'predicted incidents per 100,000 residents per year'.",
-              "title": "Unit",
-              "type": "string"
-            }
-          },
-          "required": [
-            "area_name",
-            "area_level",
-            "metric",
-            "unit",
-            "period",
-            "definition",
-            "mesh_blocks"
-          ],
-          "title": "CrimeByMeshBlock",
-          "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[CrimeByMeshBlock]",
-  "type": "object",
-  "x-fastmcp-top-level-schema": "ApiResponse_CrimeByMeshBlock_"
-}New value: +null

First observedSep 14, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and closed-world scope, so the safety profile is covered. The description adds genuinely new behavioral facts: values are modeled predictions per 100,000 residents per year, results are keyed by mesh-block code, and each call costs 20¢ — the pricing disclosure is exactly the kind of context annotations cannot carry. Return shape details are deferred to payload keys rather than described, which is a small gap.

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?

Front-loaded with the resource and granularity, then the unit semantics, then pricing. Every sentence carries information. The opening product-placement clause is arguably less useful than the rest, but it is one short clause and contextualizes the layer.

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?

With no output schema, the description does the necessary work by stating the keying scheme (mesh-block code) and the unit (predicted incidents per 100,000 residents per year), plus cost per call. It is missing only geographic/jurisdiction scope and expected result volume, which matter for an agent planning a call.

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?

Only one parameter (suburb_name) with 0% schema description coverage, so the description bears the burden. 'For every mesh block in the suburb' implicitly tells the agent the parameter scopes results to a single suburb and that output is keyed by mesh-block code, but it never explicitly defines what suburb_name accepts (suburb name string vs. code) or how matching is performed. Partial compensation, not full.

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 gives a specific verb+resource: 'Total crime rate for every mesh block in the suburb, keyed by mesh-block code.' That distinguishes it from summary-style siblings (suburbs_crime_summary, suburbs_crime_breakdown) by making clear it returns per-mesh-block granularity. It stops short of naming a sibling to contrast with, so a 4 rather than a 5.

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

Usage Guidelines3/5

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

Usage is only implied: 'Shown in the suburb report as the "Crime" map layer in the Risks section' tells the agent where this data surfaces in a product, but there is no explicit statement of when to pick this over suburban crime summaries or state-level recorded-offence tools in the sibling list. The agent must infer the mesh-block granularity is the differentiator.

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 tools Inspect

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