diagnose_app_quality

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

No annotations are provided, so the description carries full behavioral transparency. It details the return values (scores, issues, evidence locations, etc.) and input sources (code or URL). It implies a read-only diagnostic operation and does not mention any side effects. However, it could clarify required permissions or potential rate limits.

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 three sentences, each earning its place: purpose, output details, and usage guidance. It is front-loaded with the primary action and avoids redundancy. No fluff.

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 tool has 3 optional parameters, no output schema, and no annotations, the description provides sufficient context: it explains the output structure and usage timing. It could mention error handling or edge cases but is otherwise complete for an AI agent.

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%, and each parameter has a description in the schema. The tool description adds no additional meaning beyond what the schema already provides. Baseline of 3 is appropriate since the schema does the heavy lifting.

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 diagnoses UI quality for shadcn/Tailwind apps from code or a URL. The verb 'diagnose' is specific, and the resource 'app quality' is well-defined. However, it does not explicitly differentiate from sibling audit tools like 'audit_interface_craft' or 'run_audit', which may have overlapping purposes.

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 explicit guidance on when to use the tool: 'before planning UI fixes, exporting a registry, or giving an AI editor context on real app design debt.' This is clear context, but it does not mention when not to use it or name alternatives.

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