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Microburbs Australian Property Data

by au.com.microburbs
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Suburb · Parks, sport and recreation

suburbs_amenities_parks_and_recreation
Read-onlyIdempotent

Parks, playgrounds, sports grounds and courts, public swimming pools, gyms, beaches, skate parks, dog parks, golf courses and nature reserves: 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/parks-and-recreation.

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_parks_and_recreation"
  }
}

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_parks, per_1000_parks, nearest_km_parks, count_playgrounds, per_1000_playgrounds, nearest_km_playgrounds, count_sports_grounds, per_1000_sports_grounds, nearest_km_sports_grounds, count_swimming_pools, per_1000_swimming_pools, nearest_km_swimming_pools, count_gyms, per_1000_gyms, nearest_km_gyms, count_beaches, per_1000_beaches, nearest_km_beaches, count_skate_parks, per_1000_skate_parks, nearest_km_skate_parks, count_dog_parks, per_1000_dog_parks, nearest_km_dog_parks, count_golf_courses, per_1000_golf_courses, nearest_km_golf_courses, count_nature_reserves, per_1000_nature_reserves, nearest_km_nature_reserves, 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

A4.2/5.0
Behavior4/5

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 real value beyond them: the 5¢ per-call price, the geographic coverage, the data currency date, and a documentation pointer for method. What it does not disclose is whether the time-series parameters actually vary for this 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?

Two compact blocks: the substantive description first, then coverage and pricing. Every sentence carries information (metric types, geography, freshness, cost, doc link) with no restatement of the title or 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?

For a four-parameter read tool with no output schema, this covers the essentials: what is measured, where, how fresh, what it costs, and where to find the method. The one gap is the `from`/`to` parameters, which are inherited schema-wise but unexplained given a dataset described as a single 2026-10-01 snapshot.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline would be 3, but the description goes further by explaining the semantics of the field prefixes — counts within the suburb, rates per 1,000 residents, and distance from the suburb centre to the nearest instance. That interpretive layer is not present in the schema's flat field enumeration.

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?

The description names a specific resource domain and explicitly enumerates the amenity types covered (parks, playgrounds, sports grounds, pools, gyms, beaches, skate parks, dog parks, golf courses, nature reserves) plus the three metric families produced. This makes it clearly distinguishable from the sibling amenity tools such as suburbs_amenities_arts_and_entertainment and suburbs_amenities_food_and_drink.

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?

It gives useful operating context — coverage across all Australian suburbs, currency to 2026-10-01, and a pointer to /v1/datasets/parks-and-recreation for method — but never states when to choose this over siblings like properties_amenities_nearby (property-level proximity) or the other amenity categories. Usage is implied by the topic rather than guided.

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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