audit_a11y

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

Annotations declare readOnlyHint=false, destructiveHint=false, idempotentHint=false. The description adds context that the tool runs multiple engines and returns fix suggestions, but does not disclose behavioral traits beyond what annotations provide. No contradictions.

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?

Two sentences with no waste. The leading '[a11y]' tag aids quick identification. Every word adds value.

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 complexity (4 params, output schema exists, annotations present), the description covers purpose, key parameters, and optional saving. It is sufficient for an agent to understand and invoke the tool correctly.

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 parameter descriptions. The description adds value by explaining baseline_name as saving for future diff and mode as audit depth levels, going beyond the schema.

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?

Description clearly states it's a multi-engine accessibility audit on a URL, listing specific engines (axe-core, AccessLint, VertaaUX) and output type (normalized WCAG findings with fix suggestions). This distinguishes it from sibling tools like diff_a11y and run_audit.

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?

Description mentions saving findings for diff via baseline_name, providing a clear use case. However, it does not explicitly state when to use this tool vs alternatives like run_audit or analyze_component, missing explicit exclusions.

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