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

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds rich behavior: robots.txt is parsed per user-agent group rather than keyword-matched, specific files are checked, and text measurement excludes JavaScript execution. These details provide crucial context for agent decision-making and avoid common misinterpretations.

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 two sentences, front-loaded with the primary action, and every clause adds specific value without redundancy. It avoids filler and precisely communicates scope and output.

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's moderate complexity, an output schema exists (so return details are handled), and the description covers input, behaviors, output summary, plus a clear distinction from siblings. No critical context is missing; it is complete for an agent to use 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?

The schema covers the single parameter (url) with a description and example, so baseline is 3. The description does not add further parameter semantics, but none are necessary given the schema's clarity and the tool's simple input.

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 opens with a specific verb+resource: 'Audit a URL for AI visibility' and enumerates concrete checks (robots.txt parsing per user-agent group, existence of llms.txt/sitemap/JSON-LD/canonical, text accessibility without JS). This clearly distinguishes it from sibling tools like fetch_page or web_search, which have different 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 clearly implies when to use this tool—when you need to audit a URL's AI accessibility. It does not explicitly name alternative tools or exclusions, but the context is unambiguous enough for an agent to select it appropriately alongside the sibling list.

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