RBA cash rate

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden; it does disclose the semantic nature of the value (target rate, monthly averaging, annualised percent) and the schema covers the default range behaviour. It says nothing about data freshness, how far back history goes, revision policy, or that the call is a side-effect-free read, so real behavioural gaps remain.

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 sentences, no waste, and the identifying information (what the series is, its frequency and unit) is front-loaded before the usage hints. Every clause earns its place.

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 two-optional-param read tool with no output schema, the description supplies the essential semantics: unit, frequency and intended analyses. Only the shape/ordering of returned observations and the data's historical coverage are left unspecified, which is a minor gap.

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%: start and end are documented with format, pattern and omission semantics. The description adds no parameter-level detail beyond that, so the baseline 3 for schema-does-the-work applies.

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?

States a specific resource (RBA cash rate target), its transformation (monthly average) and its unit (% per year), which is exactly the level of precision needed to separate it from siblings like get_lending_rates or get_exchange_rates. An agent can tell what it retrieves without opening the schema.

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?

Gives concrete trigger phrasings ('what's the cash rate', rate hikes/cuts over time) and a comparative use case (lining rates up against CPI, rents or jobs), which implicitly points at sibling datasets. It stops short of stating when not to use it or naming an alternative tool explicitly, so it lands just under a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.