krita_generate_diffusion

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

Annotations already mark the operation as mutating, idempotent, and potentially destructive; the description adds genuinely useful behavioral context: asynchronous generation, retry semantics via operation_id, inheritance of canvas selection/regions/controls, and non-automatic pixel application. No contradiction with the annotations is present.

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 dense sentences front-load the action and then deliver the most important operational caveats in priority order. Every sentence contributes meaningful information without padding.

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 asynchronous tool with no output schema, the description omits return-value details, such as where generation_id comes from and how it is obtained. It covers the high-level lifecycle well, but the missing parameter semantics and output information leave significant gaps for correct invocation.

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?

With 0% schema description coverage, the description must compensate for missing parameter meaning, but it only explains strength and operation_id. positive_prompt, negative_prompt, seed, style_id, instance_id, and document_id are left without semantic guidance beyond their names.

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 identifies a concrete action—submitting an image through the active document's existing AI Diffusion model—and a specific resource, while clarifying that completion does not apply pixels, which helps separate it from apply/get sibling tools. It is slightly less explicit about the returned generation, but the core purpose is clear.

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?

It provides operational guidance: reuse operation_id on retries, poll generation_id separately, and remember that completion does not auto-apply pixels, implying a separate apply step is needed. It doesn't explicitly name sibling alternatives or when-not-to-use conditions, but the context is clear.

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