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Microburbs Australian Property Data

by au.com.microburbs
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Suburb · Chance of a sale within two years

suburbs_market_sale_probability
Read-onlyIdempotent

Modelled chance that a home in a suburb or city block sells within the next two years, as a percentage, for every block and suburb in Australia we score. Modelled estimates.

Coverage: About 262,000 mesh blocks and 12,100 suburbs across every state and territory; blocks we did not score return no data. Current to 2026-05-22. Full description, field list and method: /v1/datasets/sale-probability.

Price: 5¢ per call.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "suburb_name": "<suburb_name>"
    },
    "name": "suburbs_market_sale_probability"
  }
}

Input Schema

NameRequiredDescriptionDefault
toNoLast period to include, same formats as `from`.
fromNoFirst period to include: 2020, 2020-Q3, 2020-07 or 2020-07-01.
fieldsNoComma-separated subset of: sale_probability_2y_pct, dwellings_scored. Omit for all.
suburb_nameYesSuburb name exactly as /v1/suburbs/list returns it, e.g. `Bondi` or `St Kilda (Vic.)`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, and the description still adds real value: per-call pricing, the data vintage, and the important caveat that estimates are modelled rather than observed sales. It also warns that gaps in coverage yield empty results rather than errors, which prevents misreading an empty response as a failure.

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

Conciseness4/5

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

Front-loads the definition in the first sentence, then layers coverage, currency, docs pointer and price in a scannable order. Every line carries information, though the bolded fragments and the redundant restatement of 'modelled chance' / 'Modelled estimates' use slightly more space than needed.

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

Completeness5/5

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

For a four-parameter read-only lookup with full schema coverage and no output schema, this covers what an agent needs: what is measured, geographic scope, data vintage, cost per call, empty-result behavior, and where to find the field list and method. Nothing material is left dangling.

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

Parameters3/5

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

Schema description coverage is 100%, with from/to formats, the fields subset, and the exact suburb_name convention all documented inline, so the schema carries the semantics. The description adds nothing parameter-specific beyond the coverage caveat. A baseline 3 is appropriate.

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?

Names a specific resource and metric: the modelled probability, as a percentage, that a home in a given suburb sells within two years. The scope is quantified (262,000 mesh blocks, 12,100 suburbs) which makes it concrete. It does not explicitly distinguish itself from the sibling suburbs_mesh_blocks_sale_probability, which covers overlapping geography, so an agent must infer the split.

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?

Provides actionable conditions, notably that unscored blocks return no data and that results are current to 2026-05-22, plus a cost signal of 5¢ per call that should shape call discipline. However, there is no explicit when-to-use guidance and no routing to or from sibling market tools such as suburbs_mesh_blocks_sale_probability or suburbs_forecast_sale.

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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