suburbs_shapes_public_housing_sa1SA1 polygons in the suburb with their 2021 census public-housing share (only SA1s above 0%).
| Name | Required | Description | Default |
|---|---|---|---|
| geojson | No | When false, geometry is dropped — every Feature keeps its `properties` but its `geometry` is null. Use it to fetch the counts and per-feature attributes without the coordinates (default true — response unchanged). | |
| suburb_name | Yes |
Changes observed during successful MCP inspections.
Output 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": "SA1 polygons in the suburb shaded by 2021 census public-housing share.",
- "example": {
- "area_level": "suburb",
- "area_name": "Belmont North",
- "count": 7,
- "geojson": {
- "features": [
- {
- "geometry": {
- "coordinates": [
- [
- [
- 151.66,
- -32.99
- ],
- [
- 151.67,
- -32.99
- ],
- [
- 151.67,
- -33
- ],
- [
- 151.66,
- -32.99
- ]
- ]
- ],
- "type": "Polygon"
- },
- "properties": {
- "public_housing_pct": 36.9,
- "sa1": "11102130801"
- },
- "type": "Feature"
- }
- ],
- "type": "FeatureCollection"
- }
- },
- "properties": {
- "area_level": {
- "const": "suburb",
- "description": "Always 'suburb'.",
- "title": "Area Level",
- "type": "string"
- },
- "area_name": {
- "description": "Canonical ABS SAL name.",
- "title": "Area Name",
- "type": "string"
- },
- "count": {
- "description": "Number of SA1s returned (only SA1s with a non-zero share).",
- "title": "Count",
- "type": "integer"
- },
- "geojson": {
- "additionalProperties": true,
- "description": "GeoJSON FeatureCollection — one Feature per SA1 whose centroid falls inside the suburb and whose 2021 public-housing share is > 0. Properties: `sa1` (ABS SA1 code), `public_housing_pct` (0–100). Geometry is null on every Feature when the request passes `?geojson=false`.",
- "title": "Geojson",
- "type": "object"
- }
- },
- "required": [
- "area_name",
- "area_level",
- "count",
- "geojson"
- ],
- "title": "SuburbPublicHousingSa1",
- "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[SuburbPublicHousingSa1]",
- "type": "object",
- "x-fastmcp-top-level-schema": "ApiResponse_SuburbPublicHousingSa1_"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly, idempotent, and non-destructive behavior. The description goes beyond annotations by disclosing the census vintage, the polygon-per-SA1 output, and the 0% suppression rule, which helps predict the returned data shape.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One tightly worded sentence that packs the resource, operation, attribute, vintage, and filter with no redundancy. Key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only, two-parameter spatial tool, the description is nearly complete: it names the data source, the output feature type, the attribute, and the filtering rule. It could define 'public-housing share' more precisely, but no critical invocation detail is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The geojson parameter is fully documented in the schema, and suburb_name is inferable from the tool name and description. Schema coverage is 50%, but the description itself adds no explicit parameter detail, so it neither compensates strongly nor leaves a major gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States precisely that it returns SA1 polygons for a suburb with their 2021 census public-housing share, and adds a specific filter (only SA1s above 0%). This clearly distinguishes it from siblings like suburbs_shapes_boundary and properties_surroundings_public_housing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied: fetch SA1-level public-housing share geometry for a suburb. However, it does not explicitly say when to use this tool over alternatives or when not to use it, so guidance is left to inference.
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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