Japanese Article Deep Audit

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

No annotations are provided, so the description carries the full burden. It does add real behavioral context — the audit is rule-based and 'No external AI API is used', which signals determinism and that content isn't shipped to a third party. However, it never states what the audit returns (no output schema exists) nor any size/length limits, leaving notable gaps.

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?

Front-loaded with the core purpose in the first sentence, followed by a compact enumeration of checks and one closing disclosure. The check list is long but each item is a distinct capability, so little is wasted; slightly dense prose is the only cost.

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 single-parameter, annotation-free analysis tool the description covers what it is, what it checks, and the key implementation constraint (no external AI). With no output schema, it arguably should sketch the shape of the results (findings format/severity), which is the one remaining omission.

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 coverage is 100% for the single 'text' parameter (described as the Japanese article body to audit), so the schema already does the heavy lifting. The description's 'Japanese article text' phrasing aligns with but adds no format, length, or encoding detail beyond the schema.

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 verb ('audit') and resource ('Japanese article text'), then enumerates the exact rule categories checked (strong claims, unsourced numerics, duplicate/long sentences, CTA, commercial links, PR disclosure, structure). An agent knows precisely what the tool does and what it inspects, 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?

Usage is implied rather than stated: the input constraint ('Japanese article text') and the check list make it clear this is for pre-publication review of Japanese articles, but there is no explicit when-to-use, when-not-to-use, or prerequisite guidance. With no siblings there are no alternatives to route between, so the gap is moderate rather than severe.

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