check_image_spec

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that every constraint is optional, that 0 means no limit, that 'any' means no orientation requirement, and that the tool returns pass/fail with measured dimensions and failure reasons. This is useful behavioral detail, though it does not cover error cases or side effects.

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 concise and well-structured: a clear one-line purpose, then a compact explanation of constraint semantics and orientation values, then return behavior and use cases. Every sentence earns its place and the most important information is front-loaded.

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?

Given the 7 parameters, no annotations, and 0% schema coverage, the description covers the key semantics well: optional constraints, 0 meaning, orientation values, and return payload. It does not explain the backend parameter or explicitly compare itself with sibling validation tools, but an output schema exists to cover return details.

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 0%, so the description must compensate. It does add real meaning by explaining the 0-as-no-limit convention for numeric constraints and enumerating valid orientation values. However, the optional 'backend' parameter is never explained, which is a notable gap.

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 opens with a specific verb and resource: 'Check an image against a dimension specification.' It also states the return contract (pass/fail with dimensions and reasons), which clearly distinguishes it from siblings like crop_image, resize_image, or inspect_image.

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 gives clear context for when to use the tool: validating images against publishing requirements, print sizes, or asset pipeline conventions before editing. It does not explicitly exclude alternatives or name sibling tools such as fit_to_spec or inspect_image, so it stops 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.