Microburbs Australian Property Data

LGA · Languages spoken at home

lga_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": {
      "lga_name": "<lga_name>"
    },
    "name": "lga_area_stats_languages"
  }
}

Input Schema

Name	Required	Description
`to`	No	Last period to include, same formats as `from`.
`from`	No	First period to include: 2020, 2020-Q3, 2020-07 or 2020-07-01.
`fields`	No	Comma-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.
`lga_name`	Yes	Council (LGA) name exactly as /v1/lga/list returns it, e.g. `Lake Macquarie`.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 13, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely new behavioral facts: a 5¢ per-call price, a fixed one-snapshot time window, and an explicit exclusion rule for small populations — all things an agent can't read from the annotations. It stops short of describing response size or pagination behavior.

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-loads what the dataset contains, then coverage, then snapshot date, then price — each sentence carries a distinct fact with no repetition. The pointer to /v1/datasets/languages handles detail without bloating the definition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should carry more of the return-shape burden; "counts and shares" is the only hint about response structure, and nothing is said about ordering, result size, or error behavior when an LGA name is unknown. Coverage, snapshot date and pricing are complete, so the remaining gap is moderate rather than severe.

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%, so from/to/fields/lga_name are already fully documented, including the exact field list and the required LGA naming convention. The description adds only the range of language fields available (Mandarin to Nepali) and points to /v1/datasets/languages for the full field list, which is marginal beyond the schema. 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?

Names the specific dataset (Languages spoken at home), the measures (English only, 36 other languages, English proficiency, counts and shares) and the geography (suburbs and council areas). However, saying it covers "every suburb and council area" blurs the line with the sibling suburbs_area_stats_languages, so the LGA-vs-suburb scope isn't crisply distinguished.

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 selection context — the 2021-08-10 snapshot, the ~11,000 suburb / 545 LGA coverage, and exclusion of areas with too few residents — but never states when to use this versus the suburbs variant or the ancestry/religion siblings, and no alternative is named. The schema hints the LGA name must come from /v1/lga/list, but the description itself offers no routing guidance.

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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