edit_image

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

With no annotations, the description carries the full burden; it does disclose the key behavioral trait that unmasked regions stay unchanged, which is genuinely useful. It omits other relevant behavior: what is returned (one image vs n variations), whether the source image is mutated, and any input constraints such as PNG-only or size limits for masked edits.

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?

Two compact sentences with the core action front-loaded, and the mask capability stated immediately after. The bilingual restatement in parentheses is slightly redundant with the surrounding English text, but the whole thing is still short and scannable.

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 six-parameter mutation tool with no annotations and no output schema, the description covers purpose and the mask concept but says nothing about the return value (edited image(s) / URLs), model tradeoffs, or input constraints. An agent could call it, but would be guessing at what comes back.

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%, so all six parameters are already documented, giving a baseline of 3. The description adds only marginal meaning beyond the schema — that prompts accept Chinese and that the mask restricts the edited region — without clarifying model differences or how n interacts with the edit.

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?

States a specific verb and resource ('Edit an existing image based on a prompt') plus the two modes it covers (whole-image edit and masked inpainting). It implicitly contrasts with generate_image by requiring an existing image, but never names the sibling or explicitly draws the line between editing and generation.

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?

Usage is implied by 'Edit an existing image' and 'Supports inpainting with mask', so an agent can infer when a mask is appropriate. There is no explicit guidance on when to prefer generate_image or create_variation instead, and no stated preconditions (image format, size limits, model choice).

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