au-cash-rate
RBA cash rate target (F1.1 monthly average, series FIRMMCRT). Returns the most recent 12 observations (date + rate).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | Always "National" (RBA policy rate) |
RBA cash rate target (F1.1 monthly average, series FIRMMCRT). Returns the most recent 12 observations (date + rate).
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | Always "National" (RBA policy rate) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses a read-only operation ('Returns'), an output limit (most recent 12 observations), output shape (date + rate), and data provenance (F1.1 monthly average, series FIRMMCRT). It does not mention update frequency or authentication, but for a public statistical data fetch this is strong behavioral 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?
Two concise sentences with no filler. Every part contributes either the resource identity, the series identifier, or the exact return contract. The most important information is front-loaded.
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, no-output-schema read tool, the description plus schema gives an agent enough to call it correctly: no required parameters, output is known, and the source series is specified. Minor details like date format or update cadence are not included, but they are not essential for correct invocation.
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 schema already explains that the sole parameter 'region' is always 'National'. The description adds no parameter-specific detail, but none is needed because the schema handles it. This matches the baseline-3 expectation for high schema coverage.
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 (RBA cash rate target), names the exact series (FIRMMCRT), and states the action ('Returns the most recent 12 observations'). It does not explicitly contrast with sibling tools, but the resource is unambiguous enough that an agent can tell it apart. Sibling differentiation is not as explicit as in a tool that names alternatives.
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 intended use is implied: if an agent needs the latest RBA cash rate observations, this is the tool. However, there is no explicit when-to-use or when-not-to-use guidance, and no alternative tools are mentioned. The description provides enough context for a straightforward lookup, but not explicit routing.
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.