New Zealand public school term dates (nationally uniform) per the Ministry of Education, with official start-window flexibility flagged. Unknown years return an explicit notice.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Calendar year (default: current year) | |
| school_type | No | primary, secondary, or both (default) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It usefully flags official start-window flexibility and states that unknown years return an explicit notice. It does not describe the output shape, but this is a minor gap for a simple lookup tool.
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?
One compact, front-loaded sentence covers the core purpose and scope, and a second sentence adds edge-case behavior. No filler or redundant restatement of the schema.
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 description covers geographic scope, data source, national uniformity, and unknown-year behavior, while parameters are handled by the schema. Without an output schema, slightly more return-structure detail would make it fully complete, but no essential calling context is missing.
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?
Input schema coverage is 100%, with year and school_type already described in the schema. The description adds no parameter-specific semantics, so the 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?
The description names a specific resource (New Zealand public school term dates) and distinguishes it from siblings like au-school-terms and nz-public-holidays by scope and jurisdiction. It lacks an explicit main verb such as 'returns' or 'gets', so it stops short of a 5.
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?
It provides clear context: this tool is for New Zealand public school term dates, so an agent can select it over Australian or holiday-related siblings. It does not explicitly name alternatives or state when not to use it, but the jurisdictional and topical scope is unambiguous.
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.