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Microburbs Australian Property Data

by au.com.microburbs
OverviewDiscussionsAdmin

Suburb · Volunteering

suburbs_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": {
      "suburb_name": "<suburb_name>"
    },
    "name": "suburbs_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.
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

A3.7/5.0
Behavior4/5

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

Annotations already declare this a read-only, idempotent, non-destructive closed-world read, so the safety bar is met. The description adds genuinely useful operational context beyond them: the exclusion rule for small areas, the single fixed snapshot (10 Aug 2021), and a per-call price of 5¢, which is important for cost-aware invocation. It still says nothing about pagination or the shape of the returned rows.

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 measure is front-loaded in the first sentence, followed by clearly separated coverage and pricing blocks, so an agent can scan for the essentials. The coverage sentence is long and parenthetical, but every element (geography counts, exclusions, date, price, docs pointer) earns its place.

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, the description supplies the important surrounding context an agent needs: coverage scope and exclusions, a single currency date, cost per call, and a pointer to the field list so return columns can be discovered. What is missing is any mention of result volume or ordering when querying across ~10,500 suburbs, which matters for a list-style call.

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 `suburb_name` are already documented in structured form, and the description adds no syntax or format detail beyond what the schema states. It does usefully redirect to `/v1/datasets/volunteering` for the full field list, which supports the `fields` parameter, but that is the only increment.

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 the exact measure (count and share of adults aged 15+ doing voluntary work) and the geography (every suburb and council area), which lets an agent identify the metric without opening the schema. It does not address why one would pick this over `lga_area_stats_volunteering`, even though it claims to cover council areas too, leaving a small overlap ambiguity among the many area-stats siblings.

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 metric and geography, and the coverage note tells the agent when data will be absent (areas with too few people or homes). There is no explicit when-to-use / when-not-to-use guidance and no named alternative for council-area-only or other-topic lookups.

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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