macro_bank_japan_interest_rate

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable context beyond annotations by stating the date range and the return format as a pandas Series with current value in percent, which helps the agent set expectations.

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 meaning is front-loaded, but the description includes extraneous URLs and a docstring-style ':return:' block that could be cleaner. It is still concise and readable, with no redundant prose.

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?

Given no output schema, the description compensates by specifying the return type (pandas.Series) and unit (%), along with the data coverage start date. For a zero-parameter read-only tool, this is adequate, though the index or frequency of the series could be mentioned.

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 zero parameters, and the input schema is empty. The description correctly omits parameter details, and the baseline of 4 applies since there is nothing to explain.

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 tool as a report on Japan's interest rate decisions, specifies the data range from 2008-02-14 to present, and differentiates from sibling tools focused on other countries. However, it relies on the implicit verb '报告' (report) rather than an explicit action like 'get' or 'return'.

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 guidance on when to use this tool versus similar sibling tools such as macro_bank_usa_interest_rate or macro_japan_bank_rate. It lacks any exclusions or alternative recommendations.

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