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Microburbs Australian Property Data

by au.com.microburbs
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LGA · Volunteering

lga_area_stats_volunteering
Read-onlyIdempotent

How many adults do voluntary work for an organisation or group, and what share of residents aged 15 and over that is, for every suburb and council area.

Coverage: About 10,500 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/volunteering.

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_volunteering"
  }
}

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_aged_15_plus, volunteers_count, volunteers_pct, not_volunteers_count, not_volunteers_pct, volunteering_not_stated_count, volunteering_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

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent and closed-world, so the bar is lower, yet the description still adds real operational context: exact coverage counts (10,500 suburbs, 545 councils), exclusion of areas too small for reliable figures, the single snapshot date, and a 5¢ per-call price. The only omission is what an empty or suppressed area returns.

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 metric is front-loaded in the first sentence, followed by coverage, vintage, a reference for field/method details, and pricing — a sensible order with no filler. Minor redundancy in restating the vintage twice ('one snapshot at 10 August 2021. Current to 2021-08-10').

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 single-metric read tool with no output schema, the description covers what is measured, the universe, geographic granularity, vintage and cost, and defers the field list and method to a documented endpoint. It could go further by stating how missing/suppressed areas appear in results, but nothing needed to call the tool correctly is absent.

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 coverage is 100%, so `from`, `to`, `fields` and `lga_name` are already documented in the schema, and the description adds little on parameter syntax beyond contextualizing the 15-and-over denominator. The snapshot statement does implicitly warn that from/to cannot span multiple periods, which is mildly useful but not framed as parameter guidance.

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 gives a specific metric (adults doing voluntary work and share of residents aged 15+) and a specific geography (every suburb and council area), which separates it from the many other lga_area_stats_* topic tools by subject matter. It does not name the closest sibling, suburbs_area_stats_volunteering, so the LGA-vs-suburb routing still has to be inferred from the tool name.

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 context is implied by the coverage sentence (Australian LGAs/suburbs, one 2021 snapshot, small areas excluded), which tells an agent when the data applies. There is no explicit when-to-use/when-not guidance and no pointer to the parallel suburbs_* or other topic siblings, so alternative selection is left 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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