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Microburbs Australian Property Data

by au.com.microburbs
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LGA · Religion

lga_area_stats_religion
Read-onlyIdempotent

How many residents follow each religion, and what share of the area that is: 30 groups from Anglican to Sikh to no religion, for 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/religion.

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_religion"
  }
}

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_population, christianity_count, christianity_pct, anglican_count, anglican_pct, baptist_count, baptist_pct, brethren_count, brethren_pct, catholic_count, catholic_pct, churches_of_christ_count, churches_of_christ_pct, eastern_orthodox_count, eastern_orthodox_pct, jehovahs_witnesses_count, jehovahs_witnesses_pct, latter_day_saints_count, latter_day_saints_pct, lutheran_count, lutheran_pct, oriental_orthodox_count, oriental_orthodox_pct, other_christian_count, other_christian_pct, other_protestant_count, other_protestant_pct, pentecostal_count, pentecostal_pct, presbyterian_reformed_count, presbyterian_reformed_pct, salvation_army_count, salvation_army_pct, seventh_day_adventist_count, seventh_day_adventist_pct, uniting_church_count, uniting_church_pct, assyrian_apostolic_count, assyrian_apostolic_pct, christian_unspecified_count, christian_unspecified_pct, buddhism_count, buddhism_pct, hinduism_count, hinduism_pct, islam_count, islam_pct, judaism_count, judaism_pct, sikhism_count, sikhism_pct, aboriginal_traditional_count, aboriginal_traditional_pct, other_religious_groups_count, other_religious_groups_pct, other_religions_count, other_religions_pct, no_religion_count, no_religion_pct, secular_beliefs_count, secular_beliefs_pct, other_spiritual_beliefs_count, other_spiritual_beliefs_pct, no_religion_or_secular_count, no_religion_or_secular_pct, not_stated_count, 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

A3.8/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, and the description adds materially beyond them: a 5¢ per-call price, a single fixed snapshot at 2021-08-10, a minimum-population exclusion rule, and a pointer to the method documentation at `/v1/datasets/religion`. The omission of pagination/response size behavior keeps it from a 5.

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 question the tool answers, then separated into coverage, vintage, and pricing blocks that are easy to scan. The documentation pointer and price line are worth their space; the only redundancy is the vintage being stated twice ('one snapshot at 10 August 2021' then 'Current to 2021-08-10').

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only dataset query with a fully described schema and no output schema, the description supplies everything an agent needs to call it correctly and interpret it: geographic scope, exclusion caveat, fixed snapshot date, pricing, and a link to field-level and methodological detail. No output schema exists, so leaving return values to the referenced dataset docs is appropriate.

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 the schema already documents `lga_name`, `from`, `to`, and `fields` in detail; baseline is 3. The description contributes only indirectly, by stating the data is one snapshot at 2021-08-10, which signals to the agent that the date-range parameters have limited practical range, and by naming the religion groups that map to the `fields` values.

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 gives a concrete verb and resource ('how many residents follow each religion, and what share of the area') plus the field breadth ('30 groups from Anglican to Sikh to no religion'). However, it claims coverage 'for every suburb and council area' while the tool is LGA-scoped (required `lga_name`) and a `suburbs_area_stats_religion` sibling exists, which blurs the boundary rather than sharpening it.

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?

The coverage paragraph implies the geographic domain (545 council areas, 11,000 suburbs) and notes that undersized areas are excluded, which indirectly frames when the tool applies. But there is no explicit statement of when to choose this over `suburbs_area_stats_religion` or `lga_area_stats_income_by_religion`, and no prerequisites such as needing the exact LGA name from the list endpoint (that lives only in the schema).

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
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