Anymark

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

Annotations already declare readOnly, idempotent and closed-world, so safety is covered. The description adds real context: that it may return a shortened suggestion and that its rules are grounded in a study of ~1,000 logos, which helps the agent trust and interpret output.

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?

Two tight sentences with the core purpose front-loaded and the qualifying rules following. Every clause earns its place.

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 simple read-only two-param tool with full schema coverage, the description is nearly sufficient, mentioning that a suggestion may be returned. A brief note on what the verdict output looks like would close the only small gap, given no output schema.

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 both parameters are documented, so the schema does the heavy lifting. The description adds no detail on the name or business_type parameters, making the baseline 3 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 specific verb+resource (checks how a business name reads on a logo) plus a secondary action (suggests a shorter version). It is clearly distinct from create_logo_previews, which by name generates imagery rather than evaluating a name.

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?

Gives clear conditions for when the shortening logic kicks in (legal ending, web address, generic word, product word). It doesn't explicitly say when to prefer it over create_logo_previews, but the read-only evaluation role is obvious.

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?

Annotations already cover the safety profile (readOnlyHint, idempotent, non-destructive, closed-world), so the bar is lower; the description still adds meaningful context the annotations cannot: exactly six previews per call, that each links to the Anymark editor for customization, and the licensing caveat that previews cannot be used until purchase. It does not disclose latency or what happens if the business name is already taken, which keeps it from a 5.

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 front-loaded sentence gives the core count and origin, followed by useful details on composition, the editor link, licensing, and pagination. It is dense but each clause carries information; only the 'made from Anymark's designer templates with the business name set in each' phrase is slightly redundant with the following sentence.

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 must describe the return value, and it does so well (six previews, their composition, links, per-preview rationale, licensing restriction). The one gap for a 4-parameter generation tool is that the `style` parameter's effect on the previews is never explained anywhere.

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%, so the baseline is 3; the description adds real meaning by clarifying that `page` shows further previews for the *same business* and that `business_name` is rendered verbatim into the logo while `description` drives the icon-logo picks. It omits any explanation of how the `style` enum affects results.

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 names a specific verb and resource (shows 6 logo previews for a business) and details exactly what is produced: icon logos from the description, letter logos from the name's first letter, each with a rationale. This is far more than a restatement of the title and lets an agent distinguish it from check_logo_name, which is a validation lookup rather than a generation tool.

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 states the output is previews and explains that `page` (1-5) yields further previews for the same business, which implies iterative usage, but it never tells the agent when to reach for this tool versus check_logo_name or when not to call it. Usage is inferable rather than stated.

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