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Microburbs Australian Property Data

by au.com.microburbs
OverviewDiscussionsAdmin

LGA · Motor vehicles per dwelling

lga_area_stats_motor_vehicles
Read-onlyIdempotent

How many cars households keep: dwellings with no vehicle, one, two, three or four or more, for every suburb and council area.

Coverage: About 10,700 suburbs and localities and 545 council areas across Australia, in all states and territories (areas with too few people or homes for reliable figures are left out); one snapshot at 10 August 2021. Current to 2021-08-10. Full description, field list and method: /v1/datasets/motor-vehicles.

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": {
      "lga_name": "<lga_name>"
    },
    "name": "lga_area_stats_motor_vehicles"
  }
}

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: total_occupied_dwellings, no_vehicle_count, no_vehicle_pct, one_vehicle_count, one_vehicle_pct, two_vehicles_count, two_vehicles_pct, three_vehicles_count, three_vehicles_pct, four_plus_vehicles_count, four_plus_vehicles_pct, two_or_more_vehicles_count, two_or_more_vehicles_pct, vehicles_not_stated_count, vehicles_not_stated_pct. Omit for all.
lga_nameYesCouncil (LGA) name exactly as /v1/lga/list returns it, e.g. `Lake Macquarie`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent and non-destructive behavior, and the description adds genuinely new operational context: a fixed snapshot at 10 August 2021, exclusion of areas too small for reliable figures, and a per-call price of 5¢. Cost disclosure and data vintage are exactly the traits an agent cannot infer from annotations. It stops short of describing response shape or row limits, which is a minor gap without an output schema.

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?

The measure is front-loaded in the first sentence, followed by compact coverage, doc pointer and price lines. Structure is scannable with no filler, though the redundant restatement of the date ('one snapshot at 10 August 2021. Current to 2021-08-10.') costs a little.

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 no-output-schema, four-parameter data tool, vintage, coverage and cost are all present, which is good. What is missing is an unambiguous statement that results are council-area level only, so the 'for every suburb and council area' line leaves a real ambiguity about what rows come back for a required lga_name.

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?

With 100% schema description coverage, the schema already documents from/to formats, the field list, and that lga_name must match /v1/lga/list exactly. The description adds no parameter-level meaning beyond that, so the baseline of 3 applies; the geography sentence arguably muddies the meaning of lga_name rather than clarifying it.

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 opening sentence names the exact measure and its breakdown (dwellings with no vehicle through four or more), so an agent knows precisely what data this returns. However, the scope claim 'for every suburb and council area' blurs rather than sharpens the distinction from the sibling suburbs_area_stats_motor_vehicles, especially since the required parameter is lga_name.

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 when-to-use guidance at all: no statement that the suburbs_* sibling should be used for suburb-level queries, and no note that lga_name must be resolved via /v1/lga/list first (that only appears in the schema). The 'every suburb and council area' phrasing actively works against correct tool selection rather than guiding it.

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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