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Microburbs Australian Property Data

by au.com.microburbs
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LGA · Australian citizenship

lga_area_stats_citizenship
Read-onlyIdempotent

How many residents are Australian citizens, and what share of the area that is, for every suburb and council area.

Coverage: About 11,000 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/citizenship.

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_citizenship"
  }
}

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_population, australian_citizens_count, australian_citizens_pct, not_citizen_or_not_stated_count, not_citizen_or_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

A3.8/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the description only needs to add operational context — and it does: ~11,000 suburbs and 545 LGAs, exclusion of areas too small for reliable figures, a single 10 August 2021 snapshot, and a 5¢ per-call price. The price disclosure in particular is the kind of detail an agent cannot get from structured fields. It stops short of saying whether repeated calls are cached or how large a response is.

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 measure is stated first, followed by coverage, currency, and price in compact labeled blocks with no filler. Every line carries information an agent needs, and the docs pointer replaces a long field/method dump.

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 four-parameter read tool with full annotations and complete schema documentation, the description supplies scope, temporal currency, exclusions, and cost — enough to call it correctly. The only real gap is the suburb-versus-LGA scope overlap, which matters given how many near-identical siblings exist in this catalog.

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 exact `fields` enumeration, and the `lga_name` lookup convention all documented inline, so the baseline of 3 applies. The description adds nothing parameter-specific, and notably does not reconcile the single-snapshot framing with the presence of `from`/`to` range parameters.

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 states a specific measure (count and share of Australian citizens) and the geography (suburbs and council areas), so the resource is unmistakable. However, claiming coverage 'for every suburb and council area' blurs the boundary with the sibling `suburbs_area_stats_citizenship`, which an agent could easily mistake for an equivalent tool.

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?

Usage is implied by the citizenship framing and the LGA prefix in the name, and the coverage note about excluded small areas is genuinely useful context. There is no explicit statement of when to prefer this over the suburb-level sibling, and no note on how `lga_name` should be sourced beyond what the schema already says.

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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