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

Annotations cover the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true), and the description adds real behavioral context beyond them: it is a paid operation charged per image, preserves transparency, and adds no new content. It does not clarify whether the source asset is replaced or a new asset is produced.

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 short sentences with zero filler, and the core operation is front-loaded ahead of use context and pricing. Every sentence carries a distinct piece of 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?

For a two-parameter paid mutation with no output schema, the description covers cost, fidelity, and transparency preservation well. Its only gap is not stating what the call returns (e.g., a new asset_id), which the missing output schema would otherwise have to carry.

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 both parameters are already documented with constraints and the default. The description restates the 2×/4× factor choice and reinforces fidelity but adds no syntax or format detail beyond the schema, so the baseline of 3 applies.

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 and resource (make an image 2× or 4× larger and sharper) with concrete scope. It is immediately distinguishable from generate_image, animate_image, and vectorize_image, which are the neighboring image operations.

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?

"For print or large screens" gives a clear use context, and the fidelity constraint implies when it is appropriate versus generative alternatives. It does not explicitly name a when-not case or an alternative sibling, so it falls short of a 5.

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