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Microburbs Australian Property Data

by au.com.microburbs
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Suburb · Heat and temperature extremes

suburbs_environment_heat_and_extremes
Read-onlyIdempotent

How many hot days (30, 35, 40°C), hot nights, longest heatwave run and frosty days each suburb had in every year from 2016 to 2025, with the hottest day and coldest night. Modelled estimates.

Coverage: About 15,300 suburbs and localities; calendar years 2016-2025. Current to 2025-12-31. Full description, field list and method: /v1/datasets/heat-and-extremes.

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_environment_heat_and_extremes"
  }
}

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: days_over_30c, days_over_35c, days_over_40c, longest_hot_spell_days, hottest_day_c, hot_day_threshold_p99_c, nights_over_20c, nights_over_25c, frost_days, hard_frost_days, coldest_night_c. 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

A3.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds genuinely non-structured context: results are modelled estimates rather than observations, the coverage scope and currency date, and a 5¢ per-call cost. It does not describe return shape or how missing years/suburbs behave, which keeps it below 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?

The core content is front-loaded in the first sentence, with coverage, currency and pricing in short follow-up blocks. The bolded 'Modelled estimates' caveat and the price line earn their place; the docs pointer is useful rather than filler.

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 still conveys what is measured, the unit framing (days per year, °C), spatial and temporal coverage, staleness date, cost, and where to find the field list and method. The main residual gap is that the actual response shape must be discovered from the linked documentation.

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 `from`, `to`, `fields` and `suburb_name` are already documented with formats and examples. The description restates the metrics in natural language ('longest heatwave run', 'frosty days') and pins the year range, but adds no syntax or edge-case detail beyond the schema, so 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 names a concrete resource and enumerates exactly what is returned: hot-day counts at 30/35/40°C, hot nights, longest heatwave run, frost days, plus hottest day and coldest night, per suburb per year. It is highly specific, but it does not distinguish this dataset from a nearby sibling such as suburbs_environment_climate_normals, leaving the agent to infer the difference.

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?

Coverage (about 15,300 suburbs, 2016-2025) and the 'Modelled estimates' caveat imply when the data is applicable, and the price note signals a metered call. There is no explicit when-to-use or when-not-to-use guidance and no named alternative for climate normals versus yearly extremes.

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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