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

Annotations already declare readOnlyHint=true, so the trailing "Read-only" is redundant, but the description earns credit for disclosing the score scale and its interpretation bands (100 identical, 90+ visually lossless, below 50 visibly degraded) and for noting both sizes are returned. It doesn't cover failure modes for invalid or mismatched inputs, so it stops short of a 5.

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?

It leads with the metric and its output, then the interpretation scale, then usage — a sensible front-loaded order with no wasted sentences. The scale brackets are dense but each band carries decision value; the redundant "Read-only" is the only 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?

With no output schema, the description correctly carries the burden of explaining the return value (SSIMULACRA 2 score plus both image sizes) and the schema covers the preview option. For a simple two-input read tool this is close to sufficient; only error/edge behavior is unaddressed.

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 the baseline is 3; the schema already documents original, candidate, the resize-on-size-mismatch behavior, and the preview thumbnail flag. The description adds no parameter detail beyond restating the return payload (both sizes).

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 names the exact operation (visual similarity comparison between an optimised image and its original) and the precise metric returned, SSIMULACRA 2, so an agent knows this is a quality-check tool rather than a generic analyzer. It doesn't explicitly contrast itself with the sibling analyze_images, leaving that differentiation to inference.

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?

"Use it to check results" implies the post-optimization verification workflow but never states when-not to use it or names an alternative sibling (analyze_images could plausibly be confused with it). The usage context is implied rather than spelled out.

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