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Microburbs Australian Property Data

by au.com.microburbs
OverviewDiscussionsAdmin

LGA · Road crashes

lga_transport_road_crashes
Read-onlyIdempotent

Recorded road crashes by suburb and by council area per calendar year: fatal, serious-injury, other-injury and non-injury crashes, plus people killed and seriously injured, for every state and territory except the Northern Territory.

Coverage: NSW, Victoria, Queensland, South Australia, Tasmania and the ACT: 2016-2025 (Queensland to 2024); Western Australia 2021-2025. About 15,000 suburbs and 520 council areas. No Northern Territory data. Current to 2026-11-07. Full description, field list and method: /v1/datasets/road-crashes.

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_transport_road_crashes"
  }
}

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: casualty_crashes, fatal_crashes, serious_injury_crashes, other_injury_crashes, non_injury_crashes, people_killed, people_seriously_injured. 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, so the safety profile is covered. The description adds genuinely new operational context: a 5¢ per-call charge, the absence of Northern Territory data, per-state year windows (Queensland only to 2024, WA from 2021), and a pointer to /v1/datasets/road-crashes for method details.

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?

Front-loaded with what the data is, then coverage limits, then price in bold. Every sentence carries a distinct fact an agent needs before paying for a call; nothing is repeated.

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?

No output schema exists, but the description names the return metrics explicitly, so an agent knows what comes back. Combined with citation of /v1/lga/list for the required key and the external dataset reference, the definition is near-complete for a simple read-only aggregation tool; only the sibling disambiguation is missing.

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 all four parameters are already documented, including the accepted date formats and the field list. The description's enumeration of metric names loosely echoes the `fields` subset and its 'per calendar year' note hints at the `from`/`to` granularity, but it adds no format detail the schema does not already carry. Baseline 3.

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: recorded road crashes with the exact metric families (fatal, serious-injury, other-injury, non-injury, people killed/seriously injured) at LGA level per calendar year. The only wobble is the phrase 'by suburb and by council area', which blurs the distinction from the suburbs_transport_road_crashes sibling when the required parameter is lga_name.

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?

Implied guidance is present: lga_name must match /v1/lga/list, geographic coverage and year ranges are spelled out, and the price tells the agent calls are costly. But there is no explicit statement of when to choose this over suburbs_transport_road_crashes or the datasets index, so selection between siblings 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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