Stats NZ subnational Estimated Resident Population (June 2025, Aotearoa Data Explorer). Accepts region names (resolved to the region’s main territorial authority) or exact TA names, plus "Total NZ". Free API key required for the cron; request path is Redis-only.
| Name | Required | Description | Default |
|---|---|---|---|
| region | Yes | NZ region name, e.g. "Auckland", "Canterbury" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It compensates by disclosing the data vintage, the name-resolution behavior, the acceptance of exact TA names and 'Total NZ', the free API key requirement for the cron, and the Redis-only request path. It does not describe the return shape or failure cases, which prevents a 5.
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?
The description is compact and front-loaded: dataset identity first, input variants second, auth/path caveat last. Each sentence earns its place, though the 'Redis-only' phrase is terse and could be clearer for an agent unfamiliar with the infrastructure.
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 one-parameter tool with no output schema, the description covers the key facts: source, metric, geography, vintage, input forms, and auth context. It does not spell out the exact return payload or units, but those are reasonably inferable from 'Estimated Resident Population'.
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 covers the single 'region' parameter, but the description adds real semantic value: region names are resolved to the region's main territorial authority, exact TA names are also valid, and 'Total NZ' is a special accepted input. This goes beyond the schema's examples and helps an agent choose valid inputs.
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 identifies a specific dataset—Stats NZ subnational Estimated Resident Population (June 2025)—which goes well beyond the generic tool name. It clearly distinguishes this tool from siblings like nz-income, nz-place-lookup, and au-abs-demographics by naming the source agency, metric, and geographic scope.
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 description provides concrete usage context: accepted inputs include region names (resolved to the region's main territorial authority), exact TA names, and 'Total NZ'. It does not explicitly name alternatives or when-not-to-use conditions, but for a single-parameter data lookup the input guidance is clear and actionable.
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.