Ase Suggest Antialiasing

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

With no annotations, the description carries the burden of explaining side effects. It does disclose the dual behavior 'suggest (or apply)' and the 50% blend approach, which is useful. However, it does not state whether applying is destructive, whether it auto-applies by default, or what the suggestion output looks like.

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 a single concise sentence with no filler. It front-loads the core behavior and conveys the key mode distinction in just a few words.

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 tool with no annotationsable behavior, mutable or suggest-only, and four parameters, the description is too thin. The output schema may explain returns, but the description does not cover scoping, frame/layer semantics, or safety, so an agent cannot confidently invoke it in a real workflow.

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 0% and the description only weakly maps to parameters: 'suggest (or apply)' hints at the apply boolean, but sprite_path, frame, and layer are not explained at all. An agent cannot infer their meanings from the description.

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 states a clear verb+resource: detecting jagged diagonal edges and suggesting or applying 50% blend pixels. This is specific enough to distinguish it from generic drawing tools, though it leaves whether the tool mutates or only reports suggestions somewhat ambiguous in the headline.

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 no explicit guidance on when to use this tool instead of alternatives such as ase_apply_outline or ase_apply_shading. There are no stated conditions, prerequisites, or exclusions, so an agent must infer usage from the name and short phrase.

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