apply_dither

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It only states what the tool does, not whether it modifies the canvas in place, requires an existing canvas, is destructive, or returns any result. This is a significant gap for a mutation tool.

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, front-loaded sentence with no filler. It is efficient and to the point, though it lacks any additional context that would enrich structure. It earns a 4 for being tight and well-ordered.

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 simple tool with three well-documented parameters and no output schema, the description provides the bare minimum. It doesn't explain the effect on the canvas (e.g., whether it modifies in place) or the nature of the result. Given the tool's modest complexity, a score of 3 is fair – it is minimally sufficient but leaves key behavioral details uncovered.

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%, with each parameter (spread, canvas_id, matrix_size) having a clear description and defaults. The tool description adds no extra parameter information, but the baseline of 3 applies because the schema already handles parameter meaning.

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 ('applies'), a resource ('ordered Bayer matrix dithering'), and scope ('across the canvas'). This clearly distinguishes it from siblings like apply_outline (outlines) and pixelize_image (pixelation), leaving no ambiguity about its function.

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?

Gives no guidance on when to use this tool versus alternatives. It doesn't mention use cases, constraints, or scenarios where another effect might be preferred. The agent has no context to decide between this, pixelize_image, or apply_outline.

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