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Microburbs Australian Property Data

suburbs_demographics_income_map

Median weekly household income for every microburb in the suburb, plus the suburb median, national percentile and highest/lowest pockets.

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?

With no annotations, the description carries the full burden of behavioral disclosure. It does list what information is returned, including microburb-level income, suburb median, national percentile, and highest/lowest pockets, but it does not mention data sources, time periods, edge cases, or response behavior. This is adequate for a simple read-only lookup but not fully transparent.

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, dense sentence with no filler. It front-loads the core metric and immediately lists the additional outputs, making it easy to scan and understand.

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

Completeness3/5

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

For a one-parameter lookup tool with an output schema, the description covers the general purpose and key outputs, but it lacks context about when to use this specific income-map tool versus related demographics tools, and it does not clarify terms like 'microburb' or 'national percentile'. The gaps are moderate but not crippling given the tool's simplicity.

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 input schema has 0% description coverage, and the description does not compensate by explaining the suburb_name parameter format, examples, or constraints. It only refers to 'the suburb' generically, adding little meaning beyond the schema's property name.

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 the resource (median weekly household income) and the scope (every microburb in the suburb, plus suburb median, national percentile, and highest/lowest pockets). It is specific and understandable, though it does not explicitly distinguish itself from the closely named sibling tool suburbs_demographics_income or mention the map output explicitly.

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?

No guidance is provided about when to use this tool versus alternatives such as suburbs_demographics_income or suburbs_demographics_all. There is no mention of exclusions, prerequisites, or the specific use cases where the microburb-level breakdown is preferred.

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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