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

Annotations already establish read-only and idempotent behavior. The description adds valuable behavioral details: parsing robots.txt per user-agent group (not keyword-matched), checking for specific files, calculating real text without JavaScript, and returning an ordered list of fixes. This goes beyond annotations without contradicting them.

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: the first enumerates the audit components, the second states the output. It is front-loaded with the core purpose and contains no redundant or filler content.

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 fully covers what the tool checks (robots.txt, visibility files, text accessibility) and what it returns (score + fixes). With an output schema present, additional return-value detail is unnecessary. The description is complete for a complex audit tool.

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 sole parameter 'url' is fully documented in the schema with 'Page to audit'. The description doesn't add additional meaning beyond the schema, but with 100% coverage, a baseline of 3 is appropriate.

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's purpose: 'Audit a URL for AI visibility' with specific resources (robots.txt, llms.txt, sitemap, JSON-LD, canonical). It distinguishes from sibling tools like fetch_page or web_search by detailing the audit's unique checks and output (score + fixes).

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 makes the use case clear: determine which AI crawlers can access a site and what visibility signals exist. It does not explicitly mention when not to use it or name alternatives, but the specificity of the audit itself provides clear context for selection.

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