Resolves any 4-digit Australian postcode to its dominant Local Government Area (LGA, 2024) and Statistical Area Level 2 (SA2, 2021) using ABS ASGS Edition 3 correspondence tables (population-weighted ratios). Unlocks cleaner LGA/SA2 joins for demographics and building lookups.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | 4-digit Australian postcode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It clearly discloses the mapping methodology (ABS ASGS Edition 3 correspondence tables, population-weighted ratios) and the critical 'dominant' resolution behavior, which matters because postcodes often overlap multiple LGAs/SA2s. It does not describe error handling or return format, but the core behavioral traits are transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The first sentence front-loads the action, input, outputs, and methodology; the second adds practical value by stating the use case. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter lookup with no annotations and no output schema, the description covers the core behavior and use case. However, it omits expected return structure (e.g., whether codes or names are returned) and behavior for invalid or unmapped postcodes, which an agent might need to handle confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully describes the single parameter as '4-digit Australian postcode' (100% schema description coverage). The tool description repeats the '4-digit' concept but adds no new parameter-level detail, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Resolves') with a clear resource ('4-digit Australian postcode' to 'dominant LGA and SA2'), and distinguishes itself from sibling data-table tools by the postcode-to-geography mapping function. Including ABS ASGS Edition 3 and population-weighted ratios makes the purpose precise and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The final sentence implies usage context ('Unlocks cleaner LGA/SA2 joins for demographics and building lookups'), but it does not explicitly state when to use this tool versus alternatives or when not to use it. No sibling lookup tool exists among the listed siblings, yet the description still leaves the decision to inference rather than explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool maps to a clearly distinct dataset or lookup, with country prefixes and topic names separating overlapping domains. Even similar tools like au-abs-building-activity and au-abs-building-approvals are unambiguously differentiated by their descriptions.
The data tools follow a consistent country/topic hyphenated pattern (au-*, nz-*), making resource selection predictable. The meta tools (get_catalog, list_services, health) break this pattern with imperative/underscore names, but this is a minor and understandable deviation.
At 26 tools, the set is on the heavy side and slightly exceeds the typical comfortable range. However, each tool represents a genuinely distinct data service, and the clear grouping by country and topic keeps the surface navigable.
The server covers a broad range of common agent data needs for Australia and New Zealand: demographics, income, building, labour, weather, time, holidays, school terms, and place resolution. Minor gaps exist, such as no NZ building data or broader international coverage, but core workflows are well supported.