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Microburbs Australian Property Data

Suburb · Languages spoken at home

suburbs_area_stats_languages
Read-onlyIdempotent

Languages spoken at home: English only, 36 other languages from Mandarin to Nepali, and English proficiency, as counts and shares 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 residents 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/languages.

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_languages"
  }
}

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: total_population, english_only_count, english_only_pct, other_language_any_count, other_language_any_pct, english_poor_count, english_poor_pct, not_stated_count, not_stated_pct, chinese_languages_count, chinese_languages_pct, mandarin_count, mandarin_pct, cantonese_count, cantonese_pct, arabic_count, arabic_pct, vietnamese_count, vietnamese_pct, punjabi_count, punjabi_pct, greek_count, greek_pct, italian_count, italian_pct, hindi_count, hindi_pct, spanish_count, spanish_pct, nepali_count, nepali_pct, tagalog_count, tagalog_pct, korean_count, korean_pct, urdu_count, urdu_pct, tamil_count, tamil_pct, filipino_count, filipino_pct, sinhalese_count, sinhalese_pct, gujarati_count, gujarati_pct, malayalam_count, malayalam_pct, australian_indigenous_languages_count, australian_indigenous_languages_pct, indonesian_count, indonesian_pct, persian_count, persian_pct, french_count, french_pct, german_count, german_pct, bengali_count, bengali_pct, portuguese_count, portuguese_pct, thai_count, thai_pct, macedonian_count, macedonian_pct, turkish_count, turkish_pct, japanese_count, japanese_pct, serbian_count, serbian_pct, russian_count, russian_pct, croatian_count, croatian_pct, afrikaans_count, afrikaans_pct, samoan_count, samoan_pct, polish_count, polish_pct, khmer_count, khmer_pct. 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-obvious context: exclusion of low-population areas, a single dated snapshot, and a per-call price of 5¢, which materially affects invocation decisions. It stops short of describing pagination or response shape, but the added cost and coverage constraints are solid.

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?

Front-loads what the data is, then coverage, then price — a sensible ordering with no filler. Slight redundancy between 'one snapshot at 10 August 2021' and 'Current to 2021-08-10', but the price line earns its place.

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 the return content (counts and shares), grain, geographic coverage, temporal currency, and cost, plus a pointer to /v1/datasets/languages for field-level detail. Only the relationship to the LGA-level sibling and paging behavior are unaddressed.

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% and the schema already enumerates every field, the from/to date formats, and the exact suburb-name format, so the description cannot add much. It only loosely signals that the 36 languages correspond to selectable fields. 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?

States the specific resource (languages spoken at home, English proficiency) and the grain (counts and shares per suburb and council area), which lets an agent distinguish it from topical siblings like suburbs_area_stats_ancestry or suburbs_area_stats_birthplace. Minor ambiguity: it claims coverage of 'every suburb and council area' even though a separate lga_area_stats_languages tool exists, so scope overlap is not resolved.

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?

Gives useful context about when the data applies (2021 Census snapshot, areas with too few residents excluded) and a cost signal, but never explicitly says when to choose this over the LGA-level sibling or the other area-stat tools. Usage is implied by topic rather than stated.

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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