Microburbs Australian Property Data

LGA · Population projections (Qld)

lga_demographics_population_projections_qld

Read-onlyIdempotent

Projected resident population of every Queensland council area, 2021 then every five years to 2046, with five age bands. Modelled estimates.

Coverage: Queensland: 77 council areas; 2021, 2026, 2031, 2036, 2041, 2046 (figures at 30 June). Current to 2046-06-30. Full description, field list and method: /v1/datasets/population-projections-qld.

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_demographics_population_projections_qld"
  }
}

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, aged_0_14, aged_15_24, aged_25_44, aged_45_64, aged_65_plus, total_population_low, total_population_high. 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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's added value is pricing ('5¢ per call'), the modelled-estimates caveat, the exact currency date (2046-06-30) and figures-at-30-June convention — all genuinely useful and not present in structured fields. It does not describe the response shape, which is a modest remaining gap for a tool with no 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?

Front-loaded with the core scope, then coverage, currency, documentation pointer and price in tight separate blocks — an agent can parse it quickly. Minor redundancy between the title/name and the first clause, but nothing wasteful.

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 projection query with full schema coverage but no output schema, the description supplies geography count, period list, currency, age-band granularity, methodology caveat, a docs deep link and cost. The main omission is any hint of the returned record structure or row granularity (one row per LGA per period).

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 the schema already explains `from`/`to` formats, the `fields` subset and the `lga_name` matching rule. The description only adds that the data carries age bands, which loosely maps to the field list; it adds no format or interpretation detail beyond the schema. Baseline 3 applies.

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 description names a specific resource (projected resident population), a precise geography (77 Queensland council areas), a time axis (2021 then five-yearly to 2046) and a measure set (five age bands), plus a clear 'modelled estimates' qualifier. It distinguishes itself from historical/actuals tools implicitly via 'Projected' and from NSW/VIC siblings via the state scope, but never states the contrast explicitly.

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 rather than prescribed: 'Projected ... **Modelled estimates.**' tells an agent this is forecast data, not observed counts, and the coverage line bounds the scope. There is no explicit when-to-use vs. when-not guidance (e.g. versus `lga_population` or the NSW/VIC projection siblings), leaving the agent to infer selection.

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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