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

With no annotations, the description carries the full burden, and it does disclose two genuinely useful facts: the return shape ({ processedImage }) and the cost (2 credits). It omits anything about image size/latency limits, whether the source image is preserved, and whether the call is synchronous — gaps that matter for an unannotated tool.

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?

Three short sentences, front-loaded with the action, then the return value, then the cost. There is no filler and nothing an agent must read past to find the essential facts.

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 tool with a fully documented schema, the description covers purpose, return value, and cost, which is most of what is needed. The remaining gap is operational: no annotations means constraints like input size, auth, or failure behavior go unstated.

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 nested options object already documents each enhancement flag plus the default behavior when none are set. The description adds nothing about the image parameter format or how options interact, 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?

"Retouch a portrait naturally" gives a specific verb (retouch) plus a constrained resource (portrait), which is more precise than a generic image tool. It stops short of differentiating from close siblings such as enhance_image, photo_repair, or old_photo_restore, so the agent must guess which retouch-flavored tool fits.

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 explicit when-to-use or when-not-to-use statement and no named alternative, despite a crowded field of image-editing siblings. The word "portrait" and "naturally" imply scope, but nothing tells the agent whether to pick this over headshot_generator or enhance_image.

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