AutoPrint

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, closed-world), so the bar is lower. The description does add real value by disclosing a staged return: an interim status now, and the full result plus an AutoPrint link once it is ready. It stops short of describing failure modes or what the interim status looks like.

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 short sentences with no filler, and the core idea (state now, richer result later) is front-loaded. The phrasing is slightly clipped but nothing needs cutting.

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 minimal one-parameter read tool with full annotation coverage this is close to adequate, and it does gesture at the return value. With no output schema, though, the description should say more about the possible statuses and what happens on an unknown id.

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 required parameter at 100% schema description coverage ('The id start returned.'), so the schema already documents it. The description adds nothing about the id's origin or validity, leaving it at the baseline for fully-covered schemas.

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 conveys that this reports the state of an existing Print design, which is enough to separate it from the sibling start_design. However, it never states a clear verb+resource ("Where a Print design stands" is elliptical) and never names the sibling or the polling relationship explicitly, so the purpose is only implied rather than stated.

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?

There is no when-to-use/when-not guidance and the sibling start_design is not referenced. An agent can infer it should be called after starting a design, but nothing in the text confirms that or warns about calling it before an id exists.

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 flag non-read-only, non-idempotent, open-world behavior, and the description adds real context beyond them: it imports the image without modifying it, explicitly declines to generate images, place orders, or take payment, and notes the call usually finishes under a minute. The duplicate non-generation statements cost a little, but the non-goals and latency hint are genuinely useful.

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 purpose, then parameters, non-goals, and return value in a logical order. It wastes a sentence by saying the import doesn't generate or change artwork and then repeating 'does not generate images,' but the structure is otherwise tight.

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 two-parameter, non-nested tool with no output schema, the description covers the return (an id plus a studio preview link) and the boundaries of the operation. It is complete enough to call correctly, with only the check_design follow-up path left unexplained.

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 description coverage is 100% and both parameters are fully documented in the schema (HTTPS link, PNG/JPEG/WebP under 25 MB; idea describes the print). The description largely restates those facts, adding no syntax or format detail beyond the schema, so the baseline of 3 applies.

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 and resource ('Prepares existing artwork or photos for printing on wall art, apparel, or personalized gifts') and clarifies it imports rather than generates artwork. It is clear, but it never names or contrasts with the sibling check_design, so sibling differentiation is left implicit.

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: it requires an existing public image URL plus a product/idea, and it is positioned as the entry point that 'starts the work and returns an id.' However, there is no explicit when-to-use versus check_design, nor any stated prerequisite for invoking the follow-up check.

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