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

Annotations are present but sparse (readOnlyHint false, openWorldHint true, etc.). The description adds only marketing claims like 'advanced Generative AI' and 'naturally blends,' but does not disclose operational behavior such as polling, task lifecycle, required uploads, or output handling. No contradiction with annotations, but the burden of disclosure is mostly unmet.

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 padded with redundant promotional language ('No more guessing games,' 'perfect time to experiment') and ends with an incomplete 'Use case' line. It is not tightly written and contains several sentences that do not add functional value.

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?

This is an async task-based tool requiring template selection, an input image, and likely polling. The description fails to mention these prerequisites, the need to consult the Templates sibling tool, or any workflow steps. Even with an output schema, the description leaves critical usage context missing.

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 only 50% — the top-level 'request' property lacks a description. The tool description does not explain how template_id or input file parameters relate to the advertised features, nor does it mention the need to list templates first. The schema provides some parameter descriptions, but the description fails to add meaningful context.

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 clearly indicates this tool is for virtually trying on hair extensions, mentioning lengths, styles, colors, and bangs. It distinguishes itself from sibling hair tools by focusing specifically on hair extensions and blending with current hair length, though the marketing tone obscures the precise action.

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?

No explicit guidance on when to use this tool versus alternatives like AI-Hair-Style-Virtual-Try-On or AI-Hair-Color-Virtual-Try-On. The 'Use case: Suggestions for How to Shoot:' section is truncated and provides no actionable direction.

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