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

With no annotations, the description carries the full behavioral disclosure burden. It discloses the style-matching behavior, continuation of lighting and perspective, seamless blending, output dimensions and format (1024x1024 WebP), and AI review. It does not state where the output is stored or how the result is returned, but the core behavior is transparent.

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 compact sentences with no filler. The first sentence states the core action, the second explains the method, and the third specifies the output format. Every sentence earns its place.

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 one-parameter image transformation tool without an output schema, the description is complete enough for an agent to understand the input, transformation behavior, and output format. Nothing essential for selecting or invoking the tool is missing.

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%, and the only parameter input_path is fully documented in the schema. The description does not add parameter-level details, which is acceptable under the baseline rule for high schema coverage.

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 uses a specific verb ('convert') and resource ('any aspect ratio to 1:1 square'), then explains the mechanism: filling border areas while preserving original content. This clearly differentiates it from the sibling tool crop_image_to_square, which would remove content instead of expanding around it.

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 provides clear context for when to use the tool: when an image needs to become a 1:1 square without losing any original pixels. It implicitly contrasts with cropping by emphasizing border filling and pixel-perfect preservation, though it does not explicitly name alternatives or list exclusion conditions.

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