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Microburbs Australian Property Data

by au.com.microburbs
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LGA · Field of study

lga_area_stats_field_of_study
Read-onlyIdempotent

What residents studied for their post-school qualification: engineering, health, management and commerce, IT, education, creative arts, trades and more, as counts and shares for every suburb and council area.

Coverage: About 8,900 suburbs and localities and 543 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/field-of-study.

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_field_of_study"
  }
}

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_with_qualification, natural_and_physical_sciences_count, natural_and_physical_sciences_pct, information_technology_count, information_technology_pct, engineering_count, engineering_pct, architecture_and_building_count, architecture_and_building_pct, agriculture_and_environment_count, agriculture_and_environment_pct, health_count, health_pct, education_count, education_pct, management_and_commerce_count, management_and_commerce_pct, society_and_culture_count, society_and_culture_pct, creative_arts_count, creative_arts_pct, food_hospitality_and_personal_services_count, food_hospitality_and_personal_services_pct, mixed_field_programmes_count, mixed_field_programmes_pct, field_inadequately_described_count, field_inadequately_described_pct, field_not_stated_count, field_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

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds genuinely useful behavior beyond that: a 5¢ per-call price, exclusion of areas with too few people or homes, and a single fixed snapshot date. It does not describe response shape, pagination, or error behavior for an unknown LGA name.

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?

Purpose is front-loaded in the first sentence, with coverage, freshness, docs pointer, and price in a compact scannable block. The subject enumeration (engineering, health, management and commerce, IT...) largely duplicates the fields parameter enum in the schema, which is the only real waste.

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 carries return-value burden and does so adequately by stating counts and shares per area. Coverage scope, exclusions, snapshot date, and cost are all disclosed, and a documentation endpoint is provided for the field list and method. Missing only return-format specifics such as row granularity and pagination, which is minor for a single-snapshot lookup.

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 lga_name, from, to, and fields are all documented in the schema, establishing the baseline of 3. The description adds one relevant semantic point, that the data is a single snapshot dated 2021-08-10, which implicitly constrains from/to, but gives no guidance on exact-name matching failure modes beyond what the schema's example already implies.

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?

States a specific verb-and-resource pairing: post-school qualification field of study as counts and shares, for council areas. The example subject list (engineering, health, IT, trades) makes the dataset concrete. It loses a point because the phrase 'for every suburb and council area' blurs the boundary with the sibling suburbs_area_stats_field_of_study, which the name suggests is the suburb-scoped equivalent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use guidance, and no named alternative for suburb-level or other-topic lookups despite a large sibling set (occupation, education, ancestry, etc.). Coverage and snapshot date are stated, which helps an agent judge fitness, but the choice between this tool and its suburb counterpart 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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