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

With no annotations, the description carries the full burden. It reveals the return structure ({ valid, errors[], warnings[], suggestedFixes[], iconLibrary }), enumerates five specific error classes, and states that wrong-library icon imports are errors. It also discloses the default iconLibrary and the auto-suggest behavior. This is comprehensive and leaves no safety ambiguity.

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 long but information-dense, using emoji, bold, and a numbered list to guide reading. Every sentence contributes operational insight; there is no filler. It could be slightly trimmed, but the complexity justifies the length.

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?

Without an output schema, the description compensates by specifying the exact return shape and error categories. It also covers prerequisites (must import from @marmoui/ui), parameter intent, and the conditional loop for calling again. This is a self-contained contract that enables an agent to use the tool correctly and interpret results.

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%, with all three parameters already well-described in the schema. The description largely restates what the schema says (e.g., iconLibrary default and matching get_design_guidelines), adding no new parameter-level meaning. The baseline of 3 applies because the schema does the heavy lifting; the description's value lies in behavioral context, not parameter semantics.

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 clearly states the tool's function: it validates TSX/JSX code against @marmoui/ui prop signatures and returns structured feedback. It distinguishes itself from siblings like generate_component (which creates code) and validate_component_usage by being the mandatory post-generation check, with specific error categories. The verb 'validates' and resource '@marmoui/ui' make the purpose explicit.

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?

Usage is explicitly prescribed: 'MANDATORY — call this on every piece of code you generate before returning it to the user.' It also provides a clear workflow: fix errors and call again if valid=false, and instructs not to return code with errors. This is stronger than generic guidance, covering when to use the tool and the expected loop.

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