nz-ocr
Reserve Bank of New Zealand Official Cash Rate (B2 daily-close series). Trailing-12 distinct observations. Announcements are ~7-weekly.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | Always "National" (RBNZ policy rate) |
Reserve Bank of New Zealand Official Cash Rate (B2 daily-close series). Trailing-12 distinct observations. Announcements are ~7-weekly.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | Always "National" (RBNZ policy rate) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It usefully states that the series is daily-close, contains trailing-12 distinct observations, and updates roughly every 7 weeks. However, it does not describe the response format, whether anything is mutated, or how the single optional parameter affects behavior.
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?
Three terse sentences with no filler or repetition. The most identifying fact is front-loaded, and the trailing-12 and cadence details add useful specificity without bloating the description.
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 optional-parameter data tool, the description states the series, scope, and recency, which is reasonably complete. However, with no output schema, it omits the return value shape or format, and it does not clarify how the optional region parameter should be handled in practice.
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 documents the only parameter with 100% coverage: region is always 'National' per the RBNZ policy rate. The description adds no further parameter-level meaning, but the schema alone is sufficient here, so a baseline score 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 clearly identifies the resource as the Reserve Bank of New Zealand Official Cash Rate, with series code B2 from a daily-close series. It does not use an explicit verb like 'returns' or 'fetches', but the resource and scope are unmistakable and distinct from sibling tools such as au-cash-rate.
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 no explicit when-to-use or when-not-to-use guidance, and does not mention alternatives like au-cash-rate for other central bank rates. The announcement cadence and trailing-12 details are data characteristics, not usage direction.
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.