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Microburbs Australian Property Data

suburbs_market_stock_on_market

Properties currently listed for sale in the suburb, split house/unit, with the suburb's rank among its peers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
suburb_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoThe endpoint's payload, or `null` when Microburbs has no value.
reasonNoMachine-readable slug naming the no-data condition (e.g. `no_avm_for_GANSW704074813`). Stable per endpoint. Omitted on success.
messageNoHuman-readable explanation. Omitted on success.
availableNo`false` on no-data responses. Omitted on success — branch on `data !== null` if you want a single discriminator.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that the response is split by house/unit and includes a peer rank, but it leaves ambiguity about whether the result is a list or counts, what 'peers' means, and how missing or invalid suburbs are handled.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one efficient sentence with no filler. It front-loads the core subject, then adds the two key differentiating details, house/unit split and peer rank, making the content easy to parse in a tool list.

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?

This is a simple one-parameter read tool with an output schema present, so exhaustive return-field documentation isn't necessary. The description conveys the main output dimensions, though the definition of 'peers' remains vague, and it doesn't explain the comparison peer group, which is a notable gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter suburb_name has 0% schema description coverage, and the description never mentions it. While the parameter name is self-explanatory, the description adds no value about accepted format, normalization, or how the value maps to the returned data.

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 clearly states a specific resource (properties currently listed for sale in a suburb) and two distinguishing aspects: house/unit split and rank among peers. However, it doesn't explicitly differentiate itself from similar siblings like suburbs_listings_for_sale or suburbs_market_months_of_supply, so an agent may need to infer the distinction.

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

Usage Guidelines2/5

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

There is no guidance about when to use this tool instead of alternatives. The description only defines what the tool returns and does not mention use cases, exclusions, or related suburb_market_* tools that might be more appropriate for particular intents.

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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