Skip to main content
Glama
Sign In

Microburbs Australian Property Data

by au.com.microburbs
OverviewDiscussionsAdmin

LGA · Marital status

lga_area_stats_marital_status
Read-onlyIdempotent

Who is married, de facto, separated, divorced, widowed or never married, as counts and shares of adults, 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/marital-status.

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_marital_status"
  }
}

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, married_count, married_pct, separated_count, separated_pct, divorced_count, divorced_pct, widowed_count, widowed_pct, never_married_count, never_married_pct, total_in_private_households_15_plus, partnered_registered_marriage_count, partnered_registered_marriage_pct, partnered_de_facto_count, partnered_de_facto_pct, not_partnered_count, not_partnered_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 cover safety (readOnly, idempotent, non-destructive, closed-world), and the description adds genuinely non-obvious operational context on top: a stated price of 5¢ per call, an explicit exclusion rule for small areas, and a fixed 2021-08-10 snapshot. It does not say anything about response size, row limits, or pagination, which is the main remaining gap for a per-area statistics endpoint.

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 substantive content, then coverage, freshness, a documentation pointer, and price — each sentence carrying a distinct fact. The markdown-ish blocks and bolded price add scannability without bloat; the only waste is restating 'for every suburb and council area' in a tool that cannot query suburbs.

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-only statistics lookup with no output schema, this covers what an agent needs to decide and call: subject scope, geographic coverage, temporal snapshot, cost per call, and a link to the full field list. The missing piece is a hint about result shape or size, and the suburb mention adds a small ambiguity about what the tool returns.

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%, with from/to formats, the full fields enumeration, and the lga_name example all documented in the schema itself, so the baseline of 3 applies. The description's only parameter-relevant contributions are the fixed snapshot date (which tells an agent that from/to can only ever resolve to 2021-08-10) and a pointer to the dataset page for the field list — both marginal rather than additive.

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 sentence names the subject domain concretely (married, de facto, separated, divorced, widowed, never married) and the units (counts and shares of adults), which cleanly separates it from the topic-based siblings such as lga_area_stats_ancestry or lga_area_stats_religion. The geography statement is slightly muddled, though: it says 'for every suburb and council area' while the tool is LGA-scoped and requires lga_name, so the suburb half properly belongs to suburbs_area_stats_marital_status.

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 description tells you what the data is and that areas with too few people are excluded, but never states when to pick this over suburbs_area_stats_marital_status or the other LGA datasets. The one concrete selection hint is buried in the schema ('exactly as /v1/lga/list returns it'), which does help an agent construct a valid call but is not a when-to-use rule.

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 toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources