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 clearly discloses what the tool returns (URLs, colors, fonts, up to 60 image links) and its optional behavior when industry is omitted. It also mentions the measured nature of colors (実測した配色) and the direct-link format. It does not explicitly state that the operation is non-mutating, but the 'pull' nature and the context make it obvious. It could add a note on rate limits or auth, but these are likely unnecessary for a reference tool. Overall, it is transparent about expected outputs and the industry-omission case.

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 description is a single paragraph that front-loads the main purpose and then details the output and usage. It is moderately long but every sentence adds value: purpose, output content, usage mandate, and the omit behavior. There is no fluff. It could be slightly more structured (e.g., bullet points), but the flow is logical and concise for the amount of information it conveys.

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 tool with 2 parameters and no output schema, the description is highly complete. It specifies the input semantics, the output format (URLs, colors, fonts, images), and the special case for omitted industry. It does not detail the exact response structure or error handling, but given the absence of an output schema and the tool's read-only nature, these are not critical. An agent can confidently decide when to call it and what to expect from the response.

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 schema already describes both parameters fully (coverage 100%), so the baseline is 3. The description adds concrete examples for industry_or_vibe (e.g., 'カフェのLP', '士業のコーポレート') and reinforces the omit behavior, which is also in the schema but now with practical illustration. The count parameter is not elaborated further, but the schema covers its range and default. The description meaningfully enhances parameter understanding 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?

The description states a specific verb ('引く' = pull/fetch), a clear resource (実在の公開サイト = real published sites), and a precise scope: it returns URLs, measured color schemes, fonts, and up to 60 free image direct links for a given industry or vibe. It also distinguishes its purpose from generic AI design by explicitly aiming to align with real-site patterns. This fully differentiates the tool from any hypothetical alternative and leaves no ambiguity about what it does.

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 gives explicit when-to-use instructions: it should be called before creating HP/LP/UI, and for image-requiring productions it is mandated ('必ずこれを呼び'). It also instructs to place returned images in each section and avoid zero-image sections. Additionally, it explains the behavior when the industry is omitted (returns the list of covered industries). This is exemplary guidance with no ambiguity.

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