Microburbs Australian Property Data

Suburb · Long-term health conditions

suburbs_area_stats_health_conditions

Read-onlyIdempotent

How many residents live with arthritis, asthma, cancer, dementia, diabetes, heart disease, a mental health condition, stroke and other long-term conditions, and how many have more than one, 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 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/health-conditions.

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_health_conditions"
  }
}

Input Schema

Name	Required	Description
`to`	No	Last period to include, same formats as `from`.
`from`	No	First period to include: 2020, 2020-Q3, 2020-07 or 2020-07-01.
`fields`	No	Comma-separated subset of: total_population, arthritis_count, arthritis_pct, asthma_count, asthma_pct, cancer_count, cancer_pct, dementia_count, dementia_pct, diabetes_count, diabetes_pct, heart_disease_count, heart_disease_pct, kidney_disease_count, kidney_disease_pct, lung_condition_count, lung_condition_pct, mental_health_condition_count, mental_health_condition_pct, stroke_count, stroke_pct, other_condition_count, other_condition_pct, no_condition_count, no_condition_pct, any_named_condition_count, any_named_condition_pct, no_named_condition_count, no_named_condition_pct, one_named_condition_count, one_named_condition_pct, two_named_conditions_count, two_named_conditions_pct, three_plus_named_conditions_count, three_plus_named_conditions_pct, not_stated_count, not_stated_pct. Omit for all.
`suburb_name`	Yes	Suburb name exactly as /v1/suburbs/list returns it, e.g. `Bondi` or `St Kilda (Vic.)`.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 13, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so safety is covered. The description adds genuinely useful non-schema context: the coverage universe (~11,000 suburbs, 545 council areas), exclusion of low-population areas, a single fixed snapshot at 10 August 2021, and a per-call price of 5¢ — the cost signal in particular matters for agent planning.

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 what-it-returns sentence is front-loaded, followed by tight coverage/vintage and price blocks. The enumerated condition list is long but it is the substance of the dataset; there is little wasted wording, though the 'and how many have more than one' clause could be folded more cleanly.

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 simple read-only dataset tool with no output schema, the definition supplies data vintage, geographic coverage, exclusions, price and a pointer to the full field list and method, which is enough for an agent to call it correctly. The only residual gap is the exact response shape, which is delegated to a linked endpoint.

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 `suburb_name`, `from`, `to` and `fields` all documented inline including the full field list and date formats, so the schema does the heavy lifting. The description adds nothing about parameters beyond pointing to `/v1/datasets/health-conditions` for the field list and method; baseline 3 applies.

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 opens with a specific resource question — counts of residents with named long-term conditions (arthritis, asthma, cancer, etc.) broken down by suburb and council area — so an agent knows exactly what data this returns. It is distinguishable from the ancestry/religion/labour-force siblings by topic, though it never explicitly names an alternative tool.

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 agent can infer this is the tool for suburb-level health-condition data. There is no explicit statement of when to use this versus `lga_area_stats_health_conditions` (which presumably serves council-area figures) or the other `suburbs_area_stats_*` siblings, despite the description itself mentioning both suburbs and council areas.

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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