薬機法 言い換え帳 (Japanese ad-claim checker)

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

With no annotations, the description carries the full burden and does well: it discloses return shape (per-sentence 0-100% score plus rephrasing links), throughput limits, and a liability disclaimer ('Not legal advice'). It stops short of covering auth requirements or error/failure behavior, but the key behavioral traits are present.

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?

Compact and front-loaded: what it checks, the regulatory hook, the output, the limits, the disclaimer. Every sentence carries information; only the sibling-routing gap keeps it from being maximally efficient.

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?

No output schema exists, so the description correctly spells out the return values (per-sentence score and rephrasing links), and with no annotations it also supplies limits and a disclaimer. The remaining shortfall is that it omits guidance on how this relates to the sibling tool and any failure modes.

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?

One parameter with 100% schema description coverage, so the schema already explains the `text` field and its 400-character cap. The description adds the useful 'up to 5 sentences per call' constraint, but otherwise does not extend parameter meaning beyond the schema — the expected baseline when coverage is this high.

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?

Specific verb (checks/scores) plus a precise resource (Japanese cosmetics/health-food/hair-growth advertising sentences) and the regulatory context (薬機法). It is very clear what the tool does, but it never names or contrasts with the sibling scan_affiliate_articles, so an agent gets no explicit routing signal.

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 the use case (screening ad copy for bodily-effect assertions before publication) and supplies hard operating limits (5 sentences/call, 3 calls/day/user). However, it gives no explicit when-to-use vs. when-not guidance and does not position the tool against scan_affiliate_articles.

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

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

With no annotations, the description carries the full behavioral burden and does well: it discloses the result cap (6 articles), the query method (web search), the risk-flagging behavior, the returned artifacts, the 30-day retention of the shareable URL, and a per-user rate limit. It stops short of describing failure modes, latency, or whether the search is exhaustive, but the operational profile is substantially disclosed.

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?

One front-loaded sentence carries purpose, scope, and output; short trailing clauses deliver the rate limit, retention, and disclaimer with no padding. Every clause earns its place and nothing is repeated.

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?

With no output schema, the description correctly explains the return shape (a summary plus a shareable result page URL) and its 30-day lifetime. It is nearly complete for a one-parameter scan tool, though it does not describe what the summary contains or how flagged sentences are presented.

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% and the single parameter is fully documented ('Product name in Japanese (include the brand for accuracy)'). The description reinforces the Japanese-language and product-domain expectation but adds no syntax, format, or example beyond the schema, so the baseline 3 is appropriate.

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 concrete verb and resource: scanning web search for up to 6 affiliate/review articles about a Japanese cosmetics/health product and flagging 薬機法-risk sentences. The domain and scope are unusually specific, but the single sibling (check_ad_expression) is never named or contrasted, so sibling differentiation is left to inference.

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 trigger condition is clear ('for a Japanese cosmetics / health product name'), and the rate limit (2 calls/day) plus 'Not legal advice' caveat frame appropriate use. However, there is no explicit when-not guidance and no routing to the sibling tool when the task is ad-expression checking rather than article discovery.

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