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Microburbs Australian Property Data

by au.com.microburbs
OverviewDiscussionsAdmin

Suburb · Shopping and retail

suburbs_amenities_shopping
Read-onlyIdempotent

Supermarkets, convenience stores, fresh food shops, newsagents, hardware and clothing stores: counts in the suburb, rates per 1,000 residents and distance from the suburb centre to the nearest of each.

Coverage: All Australian suburbs and localities. Current to 2026-10-01. Full description, field list and method: /v1/datasets/shopping.

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_amenities_shopping"
  }
}

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: count_supermarkets, per_1000_supermarkets, nearest_km_supermarkets, count_convenience_stores, per_1000_convenience_stores, nearest_km_convenience_stores, count_fresh_food, per_1000_fresh_food, nearest_km_fresh_food, count_newsagents, per_1000_newsagents, nearest_km_newsagents, count_hardware_stores, per_1000_hardware_stores, nearest_km_hardware_stores, count_clothing_stores, per_1000_clothing_stores, nearest_km_clothing_stores, count_total, per_1000_total, per_km2_total. 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

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely useful behavior the annotations don't carry: a per-call price (5¢) and a data currency date. It stops short of describing pagination or result size, which matters for a whole-suburb dataset.

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?

Three compact blocks: what is measured and returned, coverage and currency, then docs pointer and price. Front-loaded with the resource description and free of redundant restatement of the schema.

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, so the description must carry the return-shape burden — and it does, specifying counts, per-1,000 rates and nearest-distance metrics. Coverage, currency, price and a docs pointer round it out; only result-size/pagination behavior 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 description coverage is 100%, including the full enumerated `fields` list and timestamp formats for `from`/`to`, so the baseline is 3. The description's enumeration of store categories loosely maps onto the field groups and hints at scope, but adds no syntax or semantics beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific resource (shopping/retail amenity data by suburb) and enumerates the exact store categories covered, then states the three metric families returned (counts, rates per 1,000 residents, distance to nearest). An agent can distinguish this from sibling datasets like food_and_drink or services without opening the schema.

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?

States coverage ('All Australian suburbs and localities') and currency, but gives no when-to-use or when-not guidance and does not distinguish this from the many adjacent `suburbs_amenities_*` and `suburbs_area_stats_*` siblings. Nothing tells the agent which of several amenity datasets to select.

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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