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Microburbs Australian Property Data

suburbs_hero_neighbours

Nearest neighbouring suburbs with distance in km, closest first.

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 provided, the description carries the full burden of behavioural disclosure. It does state useful behaviours: distance is in kilometres and results are ordered closest first. However, it does not mention how many neighbours are returned, whether the input suburb is excluded, what distance metric is used, or how errors like an unknown suburb 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 a single, front-loaded sentence with no filler or repetition. Every word contributes meaning, and the most important information — what is returned and how it is ordered — appears immediately.

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?

This is a simple one-parameter tool with an output schema, so the description does not need to explain return values in depth. Still, the absence of usage guidance, explicit parameter semantics, and behavioural caveats leaves the definition only minimally complete for an agent choosing among many similar suburbs_* tools.

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?

Schema coverage is 0% and the description never explicitly explains the suburb_name parameter. The phrase 'Nearest neighbouring suburbs' implies the result is relative to the named suburb, but it does not clarify expected format, examples, or any constraints. This is minimal compensation for a complete lack of schema documentation.

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 resource (nearest neighbouring suburbs) and the key output traits (distance in km, closest first). It does not explicitly distinguish itself from sibling tools like suburbs_shapes_neighbours or properties_neighbours, but the stated behaviour is specific enough to be understood.

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 given about when to use this tool versus alternatives such as suburbs_shapes_neighbours or properties_neighbours. The intended use is inferable from the output description, but there is no explicit context, prerequisites, or exclusions.

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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