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Microburbs Australian Property Data

suburbs_lifestyle_poi_impacts

What the absence of petrol stations, fast food and bottle shops near sold properties was worth.

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

C2.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full behavioral disclosure burden, but it only states a concept. It does not disclose that the tool is read-only, what the output looks like, what units are used, or any underlying assumptions—leaving the agent to infer actual behavior.

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

Conciseness3/5

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

The description is a single, short fragment with no fluff, so it is concise. However, it is structurally incomplete as a sentence and does not start with a clear action, making it less effective than a crisp two-word-plus-resource phrasing.

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?

Given the minimal description and no annotations, an output schema helps but does not fill the context gap. The description misses crucial context such as the suburb parameter's role, the nature of 'worth' (dollar amount, index?), and any time or geographic constraints, leaving the tool under-specified.

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 schema has a single required parameter, suburb_name, with 0% schema coverage logging. The description never explicitly states that the input is the suburb or how suburb_name affects the result; the connection is only implicit through the tool name and sibling grouping.

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 identifies the specific topic: the value of the absence of petrol stations, fast food outlets, and bottle shops near sold properties. It is not a tautology and is distinguishable from generic lifestyle tools, though it lacks an explicit action verb like 'returns' or 'calculates'.

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 on when to use this tool versus alternatives. It implies a use case (understanding the impact of missing amenities) but provides no exclusions or comparisons to related tools such as suburbs_lifestyle_pois or suburbs_lifestyle_summary.

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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