Suburb · Rental vacancy — per mesh block
suburbs_market_vacancy_mapCalibrated rental-vacancy rate for each mesh block in the suburb.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| suburb_name | Yes |
suburbs_market_vacancy_mapCalibrated rental-vacancy rate for each mesh block in the suburb.
| Name | Required | Description | Default |
|---|---|---|---|
| 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": "Per-mesh-block rental vacancy rates for the suburb.",
- "example": {
- "area_level": "suburb",
- "area_name": "Belmont North",
- "mbs": [
- {
- "has_listings": true,
- "mb": "10431160000",
- "rental_listings": 15,
- "rental_stock": 23.3,
- "vacancy_pct": 0.74
- }
- ]
- },
- "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"
- },
- "mbs": {
- "description": "Per-mesh-block vacancy rates.",
- "items": {
- "description": "Calibrated rental-vacancy rate for one mesh block.",
- "properties": {
- "has_listings": {
- "anyOf": [
- {
- "type": "boolean"
- },
- {
- "type": "null"
- }
- ],
- "description": "Whether the MB currently has rental listings.",
- "title": "Has Listings"
- },
- "mb": {
- "description": "ABS mesh-block code (MB_CODE21).",
- "title": "Mb",
- "type": "string"
- },
- "rental_listings": {
- "anyOf": [
- {
- "type": "integer"
- },
- {
- "type": "null"
- }
- ],
- "description": "Current rental listings in the MB.",
- "title": "Rental Listings"
- },
- "rental_stock": {
- "anyOf": [
- {
- "type": "number"
- },
- {
- "type": "null"
- }
- ],
- "description": "Estimated rental dwellings in the MB.",
- "title": "Rental Stock"
- },
- "vacancy_pct": {
- "anyOf": [
- {
- "type": "number"
- },
- {
- "type": "null"
- }
- ],
- "description": "Vacancy rate (%, calibrated to the suburb's published rate).",
- "title": "Vacancy Pct"
- }
- },
- "required": [
- "mb"
- ],
- "title": "VacancyMb",
- "type": "object"
- },
- "title": "Mbs",
- "type": "array"
- }
- },
- "required": [
- "area_name",
- "area_level",
- "mbs"
- ],
- "title": "VacancyMap",
- "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[VacancyMap]",
- "type": "object",
- "x-fastmcp-top-level-schema": "ApiResponse_VacancyMap_"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the 'calibrated' qualifier, which hints at methodology but doesn't explain what calibration means or how the rate is computed. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is efficient and front-loads the key output ('Calibrated rental-vacancy rate'). It doesn't waste words, though it could add a brief usage note without becoming bloated.
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 tool with one parameter and no output schema, the description is minimal. It doesn't explain the output format (e.g., map, GeoJSON, table), how 'calibrated' differs from raw vacancy, or how this relates to sibling vacancy tools. An agent would need to infer the return structure.
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?
Schema description coverage is 0%, so the description carries the burden for the single parameter. The description mentions 'suburb' in the text, which implies suburb_name is the suburb identifier, but it doesn't specify format, required spelling, or how to obtain valid values. Baseline 3 is appropriate because the parameter name is self-explanatory.
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?
The description states a specific verb ('Calibrated rental-vacancy rate') and resource ('each mesh block in the suburb'), which clearly identifies what the tool returns. It doesn't explicitly differentiate from sibling tools like suburbs_market_vacancy_rate or suburbs_market_vacancy_rate_series, but the mesh-block granularity is a distinguishing detail.
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?
No guidance is provided on when to use this tool versus alternatives such as suburbs_market_vacancy_rate (suburb-level) or suburbs_market_vacancy_rate_series (time series). The description implies a spatial granularity use case but doesn't state it explicitly.
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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