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

The description adds crucial context by specifying *what* the audit looks for, listing concrete examples like 'stock copy, em dashes, buzzwords,' and 'drift from the locked kit.' This adds operational knowledge that goes beyond the abstract 'readOnlyHint' annotation.

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 definition is dense but highly efficient; every phrase contributes to defining the scope of the tool. The most critical information (what it looks for and how it operates) 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?

The description is very complete in its lack of an output schema, providing all necessary context about the nature of the audit and the inputs required. It leaves no ambiguity about the tool's specific domain (design forensics).

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?

Despite a moderate schema coverage of 60%, the description provides semantic meaning by detailing which specific design elements are checked for 'drift' (e.g., 'faces, colours, radius, shadow'). This guides the agent on the underlying design constraints of the audit.

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 title and description clearly define the tool's purpose: finding 'AI giveaways' by identifying specific design 'tells' associated with generative AI. It distinguishes itself from standard audit or search tools by focusing on pattern recognition for synthetic content.

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 a clear operational guideline by stating that the tool is 'Static by default' and that 'render=true' enables deeper, 'measured checks.' This helps the agent understand the difference in complexity or thoroughness between basic and detailed audits.

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