Reserve Bank of New Zealand Official Cash Rate (B2 daily-close series). Trailing-12 distinct observations. Announcements are ~7-weekly; monthly cron keeps this current.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | Always "National" (RBNZ policy rate) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral burden. It discloses useful traits: the series is daily-close, contains trailing-12 observations, updates roughly every seven weeks via announcements, and a monthly cron keeps it current. It does not specify return format or units, but for a single-series data tool this is a solid level of disclosure.
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 short, front-loaded with the core series identity, and contains no filler. The cryptic 'B2' and the internal-sounding 'monthly cron' detail keep it from being perfectly clear, but the text remains appropriately compact.
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 single-parameter read tool with no output schema, the description conveys what data is returned (RBNZ OCR daily-close), roughly how much (trailing-12 observations), and how fresh it is (weekly announcements and monthly cron). It lacks explicit response-shape details, but the essentials needed to select and invoke the tool are present.
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 schema already fully documents the only parameter with 'Always "National" (RBNZ policy rate)', so schema description coverage is high. The tool description adds no extra parameter-level meaning beyond the policy-rate context, placing it at the high-coverage baseline.
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?
Identifies the exact economic series (RBNZ Official Cash Rate, B2 daily-close series), so an agent can distinguish it from nz-demographics and au-cash-rate siblings. It lacks an explicit verb such as 'get' or 'return', and the meaning of 'B2' is left unexplained, preventing a perfect score.
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 gives no guidance on when to select this tool over alternative tools, such as au-cash-rate or other nz-* series. The cadence note explains data freshness but not usage conditions or exclusions.
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.