inpaint_image

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds useful context by specifying the model (jimeng_image2image_dream_inpaint), the mask encoding (white=redraw, black=preserve), and special prompt behavior ('删除' to erase). However, it does not discuss side effects, reversibility, or auth requirements, which are less critical for an image editing tool but still would enhance transparency.

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 compact and front-loaded with a clear summary sentence, followed by a concise bulleted list for parameters. Every sentence earns its place, with no filler or redundancy. It is structured for quick parsing by an agent.

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 relatively simple tool, the description covers the core behavior and input requirements thoroughly. It does not need to describe return values because an output schema exists. Minor gaps include lack of mention of output format and no explicit alternative distinction, but overall the description is nearly complete for an agent to invoke the tool correctly.

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 offers only parameter names and types, with 0% coverage. The description compensates fully by detailing allowed formats (JPEG/PNG), size limit (4.7MB), mask semantics (single-channel grayscale, white/black meanings), and special prompt usage. This transforms bare schema into actionable guidance.

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 purpose: local inpainting (局部重绘) that regenerates content in a masked region based on a prompt. It is specific and distinct from generic image generation, but it does not explicitly differentiate itself from the sibling tool 'image_to_image', missing the opportunity to highlight the mask-based uniqueness.

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 provides no guidance on when to use this tool versus alternatives. It neither mentions scenarios where inpainting is appropriate nor contrasts with siblings like image_to_image or generate_image. The usage context is only implied by the parameter semantics (mask), but no explicit or even implicit 'use when' or 'avoid when' statements are present.

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