macro_bank_australia_interest_rate

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context by specifying the data range (19800201-present) and the return type (pandas.Series) with the current value in percent. This goes beyond the structured metadata without contradicting it.

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 core description is front-loaded with the essential purpose and data range. However, it includes two URLs and a docstring-style return annotation, which adds extra detail that may not be needed for tool selection. Overall, it is reasonably compact and well-structured, with a minor penalty for the cluttered inclusion of source links.

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 zero-parameter data retrieval tool, the description provides adequate contextual completeness: it states the data source, the covered period, and the return type (pandas.Series) with the unit (current value %). Combined with the strong annotations (read-only, idempotent, non-destructive), an agent has sufficient information to invoke the tool correctly without an output schema.

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 tool has 0 parameters and the input schema is an empty object, so the baseline is 4 per the rubric. The description does not need to explain parameters; it correctly focuses on output semantics. No parameter-related gaps exist.

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 Australia Reserve Bank decision report ('澳洲联储决议报告') with a specific data range (19800201 to present). Although it lacks an explicit verb like 'get' or 'retrieve', the tool name and context make the purpose unambiguous. It distinguishes from sibling tools by naming Australia, though sibling differentiation is not explicitly stated.

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 implies usage via the name and the subject ('澳洲联储决议报告'), making it clear this is for Australian central bank rate data. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or references to sibling tools like other macro_bank_interest_rate options. The guidance is thus implicit rather than directive.

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