image_to_image

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It compensates well by disclosing model choice, image format/size limits (JPEG/PNG, max 4.7MB, 4096×4096), aspect ratio options, quality settings, and the semantics of the 'scale' parameter. It does not describe output formatting, but an output schema exists to cover that.

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 concise and well-structured. It opens with a clear purpose statement, then provides a compact bulleted list of parameter details. Every sentence adds value, and there is no redundant or extraneous content.

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?

The tool is relatively simple with 5 parameters, and the description covers most of them thoroughly. It includes model version and input constraints. It does not explicitly differentiate from sibling tools (like inpaint_image), and the 'prompt' parameter could use a bit more elaboration, but overall it is sufficient for an agent to confidently invoke the tool.

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?

The input schema provides only type/defaults with no property descriptions (0% schema description coverage), so the description must add meaning. It explains image_url constraints, allowed aspect ratios, quality choices (2k default vs normal), and scale's effect (0.0-1.0, larger follows text, smaller preserves the original). The 'prompt' parameter is implicitly covered via '结合文字描述' but lacks explicit format details.

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 states the tool's function: '图生图（智能参考）：参考输入图片，结合文字描述编辑或生成新图片' (image-to-image with smart reference: edit or generate new images based on an input image and text description). It also specifies the underlying model (jimeng_i2i_v30), making the purpose specific and distinct from siblings like generate_image or inpaint_image.

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 usage context by emphasizing '参考输入图片' (reference input image) and '结合文字描述' (combined with text description), suggesting it is for image-conditioned generation. However, it does not explicitly state when to prefer this tool over alternatives such as inpaint_image or generate_image, nor does it provide exclusions or alternative recommendations.

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