suburbs_market_price_growthPast compound sale-price growth over 1 / 3 / 5 / 10 years, by property type (decimal). The 3y / 10y figures are the same ones the Suburb Finder ranks on.
| 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": "Past compound sale-price growth over 1 / 3 / 5 / 10 years, by type.\n\n`house` and `unit` are objects keyed by horizon — `'1yr'`, `'3yr'`,\n`'5yr'`, `'10yr'` — each a decimal (0.42 = +42%). The 3y / 10y values are\nthe same figures the Suburb Finder ranks on (`growth3yrCagr` /\n`growth10yrCagr`).",
- "example": {
- "area_level": "suburb",
- "area_name": "Belmont North",
- "as_of": "2026-05-24",
- "house": {
- "10yr": 1.24,
- "1yr": 0.08,
- "3yr": 0.36,
- "5yr": 0.684
- },
- "unit": {
- "10yr": 0.61,
- "1yr": 0.02,
- "3yr": 0.04,
- "5yr": 0.06
- }
- },
- "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"
- },
- "as_of": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "null"
- }
- ],
- "description": "Date of the most recent observation (YYYY-MM-DD).",
- "title": "As Of"
- },
- "house": {
- "anyOf": [
- {
- "additionalProperties": {
- "anyOf": [
- {
- "type": "number"
- },
- {
- "type": "null"
- }
- ]
- },
- "type": "object"
- },
- {
- "type": "null"
- }
- ],
- "description": "House compound growth by horizon: keys '1yr','3yr','5yr','10yr' (decimal).",
- "title": "House"
- },
- "unit": {
- "anyOf": [
- {
- "additionalProperties": {
- "anyOf": [
- {
- "type": "number"
- },
- {
- "type": "null"
- }
- ]
- },
- "type": "object"
- },
- {
- "type": "null"
- }
- ],
- "description": "Unit compound growth by horizon, same keys (decimal).",
- "title": "Unit"
- }
- },
- "required": [
- "area_name",
- "area_level"
- ],
- "title": "PriceGrowthAll",
- "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[PriceGrowthAll]",
- "type": "object",
- "x-fastmcp-top-level-schema": "ApiResponse_PriceGrowthAll_"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that the call is read-only, idempotent, and non-destructive, so the description doesn't need to repeat that. It adds value by specifying decimal units, compound growth, property-type breakdown, and alignment with Suburb Finder figures; it doesn't cover output shape or data-availability caveats, but these are secondary given the strong 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?
Two sentences with no filler; the core output facts come first, and the Suburb Finder alignment note is a useful second sentence. The description is appropriately sized for the tool.
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?
The description communicates the essential output semantics: time horizons, property types, decimal format, and ranking context. However, with no output schema it doesn't describe the response structure, property-type values, or behavior when a suburb lacks enough sales data, so an agent must assume some details.
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 input schema only contains suburb_name, but the description never mentions the parameter or how to supply a valid suburb name, and schema description coverage is 0%. The schema's property name is self-explanatory, yet the description does not compensate for the low coverage as required.
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 identifies a specific data resource: past compound sale-price growth across 1/3/5/10-year horizons, broken down by property type and expressed as a decimal. This goes beyond the title and distinguishes it from single-window siblings like suburbs_market_price_growth_5y, though it doesn't name that sibling explicitly.
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?
There is no explicit when-to-use or when-not-to-use guidance, and no alternative tool is named. The note that 3y/10y figures match what Suburb Finder ranks on provides some context, but it leaves the agent to infer the tool's role relative to the many other suburbs_market_* endpoints.
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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