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

With only openWorldHint=true in annotations, the description carries most of the behavioral burden and delivers the key trait: this is asynchronous, returning a request ID rather than the image, retrieved later via fetch-image. It omits cost, rate limits, auth, and whether status can be polled instead of fetched.

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 lines, front-loaded with the core action, then the required inputs, then the return-path. Every sentence earns its place with 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 8-parameter generation tool with full schema coverage and no output schema, the description covers the essential lifecycle: inputs required, async return, and retrieval tool. Minor gap in not mentioning the webhook alternative to fetching results, but nothing critical is 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 100%, so all 8 parameters are already documented in the schema, establishing a baseline of 3. The description restates the mask/prompt/image triad and confirms the mask semantics (edit vs preserve), adding little beyond what the schema already states verbatim.

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 specific areas of images using masks') and immediately disambiguates from text-to-image/image-to-image by naming the mask-based workflow. An agent can identify this as the masked-region editing tool without opening the schema.

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 gives the required operating context (supply image, mask, prompt) and names the follow-up tool fetch-image for retrieving results, which is clear temporal guidance. It stops short of stating when NOT to use it, e.g. versus image-to-image for whole-image edits or song-inpaint for audio.

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