Resolves any NZ suburb or locality name to its Territorial Authority and Stats NZ population estimate via the LINZ Suburbs and Localities dataset (~5,900 places). Exact-name match plus prefix fallback. The join key that unlocks NZ crime/demographics/income lookups.
| Name | Required | Description | Default |
|---|---|---|---|
| suburb | Yes | NZ suburb or locality name, e.g. "Ponsonby" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses useful behavior beyond the schema: exact-name match with prefix fallback, dataset source, and approximate place count. However, with no annotations provided, it does not describe no-match behavior, ambiguity handling, or output format, leaving meaningful gaps.
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 crisp sentences front-load the core purpose and immediately add scope, matching behavior, and usage context. Every phrase earns its place with no filler.
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?
The tool is simple with one parameter, but there is no output schema and no annotations. The description omits failure/fallback ambiguity behavior and return shape, so an agent may not know what happens on no match or how results are structured.
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?
Schema description coverage is 100%, and the parameter schema already includes an example ('Ponsonby'). The tool description adds no extra parameter-level semantics beyond the schema, so baseline 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?
States a specific verb ('Resolves') plus the resource ('any NZ suburb or locality name') and the expected outputs (Territorial Authority, Stats NZ population estimate). The LINZ dataset reference further anchors what the tool does and sets it apart from the data-query siblings.
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 join-key sentence clearly implies when this lookup should be used: before NZ crime, demographics, or income lookups. It does not name alternatives or explicitly say when not to use it, but the intended context is clear.
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.