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

Annotations already indicate a mutating, idempotent, non-destructive op, but the description adds significant behavioral context: immediate point deduction (count×25), refunds for failed images, non-cancellation after acceptance, and per-image pricing. No contradiction exists.

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 Korean sentences each carry distinct operational information: scope, cost/refund behavior, and cancellation policy. The core task is front-loaded and there is no filler.

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 an async tool with no output schema, the description covers submission, pricing, refunds, and cancellation well. It does not state what the response contains (e.g., a job ID) or how to retrieve results, though sibling tools image_batch_result and image_batch_status strongly imply the intended flow.

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%, so the schema already documents all 9 parameters. The description only adds cost semantics related to image_count, which is useful but does not compensate or expand parameter meaning 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 (접수합니다) and a clear resource: asynchronous generation or editing jobs for 1–50 images. The batch size and async nature distinguish it from single-image siblings like image_generate and image_edit.

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 implies batch use through '1~50장' and '비동기' but does not explicitly say when to choose this over single-image alternatives. No direct comparison or exclusion of image_edit/image_generate is provided.

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