macro_bank_japan_interest_rate

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

Annotations already declare read-only, idempotent, non-destructive, open-world behavior, so the safety profile is covered. The description adds the data coverage window (20080214-至今) and the return payload (今值 in %) with rtype pandas.Series, which is genuinely useful beyond the annotations, but says nothing about update cadence, source latency, or freshness.

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 sentence and the return annotation are front-loaded and useful, but two raw source URLs are embedded in the description text, which is noise that does not help an agent decide or invoke. Trimming the URLs would sharpen it considerably.

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 retrieval tool with no output schema, the description supplies the coverage range and the return type/value semantics (:return: 今值 (%), :rtype: pandas.Series), which is what an agent needs to interpret the result. Nothing critical is missing 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?

The tool takes zero parameters, so the schema fully covers invocation and the baseline is 4. The description correctly avoids inventing parameter semantics that do not 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?

States the specific resource (Japan interest rate decision report) and its temporal coverage from 2008-02-14 to present. An agent can identify it as the Japan rate-decision dataset, but the description does not distinguish it from close siblings like macro_japan_bank_rate or macro_bank_usa_interest_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?

No statement of when to use this tool versus the numerous sibling interest-rate tools, and no preconditions or exclusions. The agent must infer usage entirely from the name and the Japan/rate-decision wording.

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