post-inpaint-inferences

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 mentions that the tool triggers image generation and describes the mask's function, but fails to cover critical aspects like authentication requirements, rate limits, cost implications, error handling, or the nature of the output (e.g., whether it returns generated images or a job ID). This is inadequate for a complex mutation tool with 26 parameters.

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 extremely concise with only two sentences, front-loading the core purpose without any redundant information. Every sentence earns its place by clearly stating the action and key parameter role, making it efficient and easy to parse.

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?

Given the tool's complexity (26 parameters, no annotations, no output schema), the description is insufficient. It lacks details on behavioral traits (e.g., side effects, response format), usage context, and does not explain return values, leaving significant gaps for an AI agent to understand how to invoke and interpret results effectively.

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 description adds minimal parameter semantics by explaining the mask's purpose ('indicates the area to inpaint'), but with 92% schema description coverage, most parameters are already well-documented in the input schema. The description does not compensate for the few gaps (e.g., 'dryRun' lacks a schema description) or provide high-level context beyond what the schema offers, meeting the baseline for high coverage.

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 with a specific verb ('Trigger') and resource ('image generation in Inpaint mode'), and distinguishes it from siblings by specifying the mode (Inpaint) and the role of the mask, which is unique among sibling tools like post-img2img-inferences or post-txt2img-inferences.

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, such as other inference tools like post-img2img-inferences or post-controlnet-inpaint-inferences. It mentions the mask's role but does not specify scenarios, prerequisites, or exclusions for usage, leaving the agent without contextual direction.

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