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Microburbs Australian Property Data

suburbs_market_vacancy_rate_series

Full rental-vacancy history by property type, decimal fractions.

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

B3.1/5.0
Behavior3/5

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

There are no annotations, so the description carries the burden. It adds meaningful behavioral context by stating that the output covers the full history, is broken down by property type, and uses decimal fractions. It does not disclose data availability, missing-suburb behavior, or included property types, but for a simple read tool the core data shape is communicated.

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 a single compact sentence with no filler. It front-loads the core resource ('full rental-vacancy history') and packs the key differentiators ('by property type', 'decimal fractions') efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, but the definition does not compensate for the 0% parameter description coverage and offers no usage guidance relative to sibling vacancy tools. The single required parameter is undocumented in the description, making reliable invocation uncertain despite the tool name hinting at suburb_name.

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

Parameters1/5

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

The schema has one required parameter, suburb_name, with no description (0% coverage), and the tool description never mentions the parameter, its expected format, or examples. The agent is left to infer that a suburb name string is accepted, with no validation guidance.

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 states the resource clearly: 'full rental-vacancy history' grouped 'by property type', and adds a useful representation detail ('decimal fractions'). It does not explicitly name sibling tools like suburbs_market_vacancy_rate or suburbs_market_vacancy_map, but 'full history' distinguishes it from point-in-time vacancy tools.

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?

No explicit when-to-use or when-not-to-use guidance is provided, and no alternatives are named. The word 'history' weakly implies this is for time-series needs rather than a current vacancy rate, but the agent must infer the selection criteria from the tool name and sibling context.

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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