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Microburbs Australian Property Data

by au.com.microburbs
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Suburb · Education and qualifications

suburbs_area_stats_education
Read-onlyIdempotent

How far residents went at school and the highest qualification they hold since: Year 12 to Year 8 or below, and postgraduate, bachelor, diploma and certificate levels, 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/education.

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_education"
  }
}

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_left_school, year_12_count, year_12_pct, year_11_count, year_11_pct, year_10_count, year_10_pct, year_9_count, year_9_pct, year_8_or_below_count, year_8_or_below_pct, did_not_go_to_school_count, did_not_go_to_school_pct, school_not_stated_count, school_not_stated_pct, total_aged_15_plus, bachelor_or_higher_count, bachelor_or_higher_pct, postgraduate_degree_count, postgraduate_degree_pct, graduate_diploma_or_certificate_count, graduate_diploma_or_certificate_pct, bachelor_degree_count, bachelor_degree_pct, advanced_diploma_or_diploma_count, advanced_diploma_or_diploma_pct, certificate_count, certificate_pct, certificate_3_or_4_count, certificate_3_or_4_pct, certificate_1_or_2_count, certificate_1_or_2_pct, certificate_level_unspecified_count, certificate_level_unspecified_pct, qualification_inadequately_described_count, qualification_inadequately_described_pct, qualification_level_not_stated_count, qualification_level_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

A3.8/5.0
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 lookup, so the safety profile is covered. The description goes further with details the annotations cannot carry: roughly 10,500 suburbs and 545 council areas, a single snapshot dated 10 August 2021, exclusion of low-population areas, a pointer to /v1/datasets/education for method and field list, and an explicit price of 5¢ per call. The per-call cost disclosure is especially valuable for an agent deciding whether to batch queries.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Structure is front-loaded and purposeful: what the data is, then coverage, then vintage, then a documentation pointer, then price. Each block earns its place and no sentence repeats the tool name or restates structured data. The bolded price line is a scannable emphasis rather than padding.

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 statistical lookup with no output schema, the description supplies the essentials an agent needs: subject scope, geographic coverage, data vintage, exclusion caveat, cost, and a route to the full field/method documentation. The main residual gap is that nothing describes the response shape or how rows are keyed, which matters given the wide, flat set of count/pct field pairs.

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%, with from/to formats, the full fields allow-list, and the suburb_name example all documented inline, so the baseline is 3. The description adds some topical framing that maps to the field families (Year 12 through Year 8, postgraduate/bachelor/diploma/certificate), but nothing about parameter behaviour — e.g. how from/to interact with a single-date snapshot, or what happens if a name isn't matched exactly.

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 subject matter (schooling attained and highest qualification held) and enumerates the categories covered, from Year 12 down to Year 8 or below plus postgraduate through certificate levels. It also states the geographic unit (suburb and council area), which separates it from the topic-based siblings like ancestry, religion or labour force. It stops short of explicitly distinguishing itself from near neighbours such as suburbs_area_stats_field_of_study or lga_area_stats_education, so a 5 isn't warranted.

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 topic rather than stated: an agent must infer that this is the tool for education/qualification questions about a suburb. There is no 'use this instead of X' routing, no prerequisites, and no note on how it relates to the LGA-scoped sibling. It does add a useful eligibility rule (areas with too few people or homes are excluded), which helps an agent interpret empty results.

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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