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

With no annotations, the description should carry the behavioral load, and it does disclose input-size limits, that scale 4 runs the model twice and is slower, and that the returned download URL expires after one hour. It omits error behavior and any cost/quota implications, keeping it 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 is front-loaded with the core action and mechanism, then limits and return behavior; the parenthetical '(2.25 on Pro)' and 'runs the model twice' are slightly compressed but every sentence carries information.

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?

There is no output schema, so the description correctly explains the return value (one-hour download URL), and it covers size limits and scale trade-offs; the main gap is what happens on oversized inputs or failures.

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 coverage is 100%, so the baseline is 3; the description adds real meaning by tying `scale` to Pro-only status, a 1MP input cap, and extra latency, beyond the schema's terse '2 (default) or 4 (Pro)'.

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?

It states a specific verb and resource ('Double an image's width and height') plus the mechanism (super-resolution model reconstructing edges/texture), which clearly separates it from compress_image or convert_image even though no sibling is named explicitly.

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 gives useful preconditions (input up to ~1MP, 2.25 on Pro; scale 4 requires Pro and is slower) but never says when to prefer this over alternatives like convert_image or run_pipeline, so the usage decision is only implied.

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