run_llm_audit

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

Annotations provide basic safety hints, and the description adds key behavioral context: it consumes LLM credits and operates via an external adapter. No contradiction, but could further disclose side effects like persistence.

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 extremely concise: two sentences plus a contrast line, front-loaded with the core function. Every sentence adds value with no redundancy.

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 existence of an output schema, the description sufficiently covers purpose and usage boundaries. It could mention prerequisites like URL accessibility but remains largely complete.

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?

Input schema covers all three parameters with descriptions (100% coverage). The tool description adds no additional parameter detail beyond indicating the nature 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 description clearly states it performs an LLM-based UX audit on a URL, listing specific qualitative aspects like hero clarity and messaging, and distinguishes itself from the rule-based sibling audit_url.

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?

Explicitly contrasts with audit_url (rule-based WCAG/a11y) and notes LLM credit consumption, providing clear guidance on when to use this tool and its resource implications.

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