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Microburbs Australian Property Data

by au.com.microburbs
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Suburb · Households and families

suburbs_area_stats_households
Read-onlyIdempotent

Household and family make-up: lone-person, family and group households, household size, couples with and without children and one-parent families, for every suburb and council area.

Coverage: About 10,600 suburbs and localities and 545 council areas across Australia, in all states and territories (areas with too few residents 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/households.

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_area_stats_households"
  }
}

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_households, total_families, family_households_count, family_households_pct, non_family_households_count, non_family_households_pct, lone_person_households_count, lone_person_households_pct, size_1_person_count, size_1_person_pct, size_2_people_count, size_2_people_pct, size_3_people_count, size_3_people_pct, size_4_people_count, size_4_people_pct, size_5_people_count, size_5_people_pct, size_6_plus_people_count, size_6_plus_people_pct, couple_no_children_count, couple_no_children_pct, couple_with_children_count, couple_with_children_pct, one_parent_families_count, one_parent_families_pct, other_families_count, other_families_pct, couple_children_under_15_count, couple_children_under_15_pct, one_parent_children_under_15_count, one_parent_children_under_15_pct, couple_families_count, couple_families_pct. 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

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds meaningful behavioral context beyond that: the exclusion of areas with too few residents, the single fixed snapshot date, the 10,600/545 coverage counts, and — unusually valuable — an explicit per-call price of 5¢, which an agent should weigh before calling. It stops short of describing the response shape or whether the road-tested `/v1/datasets/households` link is required reading for interpretation.

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?

Well front-loaded: the substantive data content comes first, followed by coverage/currency, documentation pointer, and price. Minor waste in restating the snapshot twice ('one snapshot at 10 August 2021' and 'Current to 2021-08-10'), but no sentence is otherwise padding.

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

Completeness4/5

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

For a read-only data-retrieval tool with no output schema, the definition covers subject matter, geographic coverage, currency, cost and where to find the field list and method — enough to call correctly. The remaining gap is the unexplained tension between the `from`/`to` period parameters and a single fixed snapshot, plus the ambiguity about whether suburb-level calls return council-area figures.

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 `fields` enumerating all 34 columns and `suburb_name` giving format examples, so the schema does the heavy lifting and baseline is 3. The description adds no syntax or constraint detail beyond pointing at the dataset documentation for the field list. It also does not explain how the `from`/`to` period parameters behave given the data is a single 2021 snapshot.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the exact subject matter — household and family composition — and enumerates the constituent measures (lone-person, family and group households, household size, couples with/without children, one-parent families). This is specific enough that an agent can distinguish it from the many other `suburbs_area_stats_*` topic tools (ancestry, religion, income) without inspecting the schema.

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?

Coverage information (about 10,600 suburbs and 545 council areas, snapshot at 10 August 2021) is genuinely useful context for deciding whether this tool answers a question, but there is no explicit when-to-use or when-not-to-use guidance, and no routing to alternatives. Notably, the description claims the data covers 'every suburb and council area' while a sibling `lga_area_stats_households` exists and the single required parameter is `suburb_name`, leaving the suburb-vs-council-area choice to inference.

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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