Microburbs Australian Property Data

LGA · Birthplace

lga_area_stats_birthplace

Read-onlyIdempotent

Where residents were born: counts and shares for the top 43 countries, plus Australia and other, in 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 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/birthplace.

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_birthplace"
  }
}

Input Schema

Name	Required	Description
`to`	No	Last period to include, same formats as `from`.
`from`	No	First period to include: 2020, 2020-Q3, 2020-07 or 2020-07-01.
`fields`	No	Comma-separated subset of: total_population, australia_count, australia_pct, england_count, england_pct, india_count, india_pct, china_count, china_pct, new_zealand_count, new_zealand_pct, philippines_count, philippines_pct, vietnam_count, vietnam_pct, south_africa_count, south_africa_pct, malaysia_count, malaysia_pct, italy_count, italy_pct, sri_lanka_count, sri_lanka_pct, nepal_count, nepal_pct, scotland_count, scotland_pct, south_korea_count, south_korea_pct, united_states_count, united_states_pct, germany_count, germany_pct, hong_kong_count, hong_kong_pct, iraq_count, iraq_pct, greece_count, greece_pct, pakistan_count, pakistan_pct, lebanon_count, lebanon_pct, indonesia_count, indonesia_pct, thailand_count, thailand_pct, ireland_count, ireland_pct, iran_count, iran_pct, fiji_count, fiji_pct, netherlands_count, netherlands_pct, singapore_count, singapore_pct, afghanistan_count, afghanistan_pct, bangladesh_count, bangladesh_pct, canada_count, canada_pct, taiwan_count, taiwan_pct, brazil_count, brazil_pct, poland_count, poland_pct, japan_count, japan_pct, croatia_count, croatia_pct, egypt_count, egypt_pct, north_macedonia_count, north_macedonia_pct, zimbabwe_count, zimbabwe_pct, myanmar_count, myanmar_pct, cambodia_count, cambodia_pct, turkey_count, turkey_pct, france_count, france_pct, malta_count, malta_pct, not_stated_count, not_stated_pct, other_countries_count, other_countries_pct. Omit for all.
`lga_name`	Yes	Council (LGA) name exactly as /v1/lga/list returns it, e.g. `Lake Macquarie`.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 13, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare this a safe, idempotent, closed-world read, but the description adds genuinely new behavior: the record universe (about 11,000 suburbs and 545 councils), the census vintage and currency (snapshot 10 August 2021, current to 2021-08-10), small-area suppression, and a per-call price of 5¢ that an agent should weigh before repeated calls. It stops short of describing pagination or response shape, so it is strong rather than exhaustive.

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?

Led by the substantive what-you-get sentence, followed by a compact coverage block, the reference link, and the price. Every line carries information, though the phrase 'in every suburb and council area' should be tightened to match the LGA scope.

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?

With no output schema and four fully documented parameters, the description supplies what the schema cannot: metric definitions (counts and shares), geographic and temporal coverage, suppression behavior, and cost. The remaining gap is disambiguation from the suburb-level birthplace tools and from adjacent LGA datasets, which the coverage sentence arguably muddies.

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%, so from/to, fields and lga_name are already documented with formats and an example. The description's only added meaning is that the field set is oriented around the top 43 countries plus Australia and other, which lightly contextualizes the long `fields` enumeration; nothing is said about how from/to behave for a single-snapshot dataset.

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 subject and metric: birthplace counts and shares for the top 43 countries plus Australia and other. The scope claim 'in every suburb and council area' is confusing for a tool named LGA and titled 'LGA · Birthplace', since siblings suburbs_area_stats_birthplace and suburbs_ethnicity_birthplace already cover the suburb level, so the agent gets no clean differentiation from those.

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 only implied: the coverage sentence tells the agent the tool answers LGA-level birthplace questions, and 'areas with too few residents ... are left out' warns of missing rows. It never names the suburb sibling or the adjacent LGA datasets (ancestry, citizenship, migration, year_of_arrival) nor states when to pick this one over them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Microburbs Australian Property Data tools Inspect

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