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

With no annotations, the description carries the full burden of behavioral disclosure. It explains the cumulative transform semantics precisely (offset by i, rotation by i, scale by d_scale^i), states that styles are copied, and suggests how to animate the result. It doesn't explicitly say the original remains unchanged or describe the returned structure, but the cloning behavior is well implied.

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 front-loaded with the core definition, then provides a compact formula, avoids redundancy with the schema, and uses brief illustrative examples to clarify intended use. Every sentence contributes to understanding the tool's purpose, behavior, or typical application.

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 7-parameter primitive with no output schema and no annotations, the description is thorough: it explains the transform model, enumerates acceptable element types, gives usage patterns, and notes animation follow-ups. It does not specify z-ordering or whether copies are independent layers, but these are minor omissions for an agent invoking this tool.

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?

Although the schema covers each parameter at 100%, the description adds essential semantics beyond the schema: the mathematical relationship between parameters and copies, exponential scaling via d_scale^i, and meaningful parameter combinations. This is exactly the kind of clarification an agent needs to call the tool correctly.

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-resource pair: 'clone a leaf ... count times', and names the exact element types it accepts (image, video, shapes, text). It clearly distinguishes this from duplicate_project and other sibling operations by framing it as a 'composition primitive' that applies cumulative transforms.

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 concrete use cases ('a circle of stars', 'a row of chevrons', 'a fractal', 'a staggered grid') and claims 'One call instead of dozens', implying the alternative is repeated add/duplicate calls. However, it does not explicitly name alternative tools or state when not to use this tool, so there is a small gap in routing guidance.

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