Audit Agent Accessibility

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

The description adds substantial behavioral context beyond the annotations: 'No model is called at any point', 'same domain returns the same row today and next month unless the site actually changed', 'Twelve requests per domain, typically 2 to 4 seconds', and 'Read only'. It also discloses resource consumption and auth needs, aligning with the annotations without contradiction.

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 relatively long but every sentence adds value: it covers output structure, behavior, performance, use case, and requirements. It is front-loaded with the core purpose and then expands into detail. Minor verbosity exists, but it is well-organized and not redundant.

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?

Despite having no output schema, the description thoroughly describes the return value as a flat row of 42 fields across five named families. It also covers deterministic behavior, request count, latency, auth, and intended audience. For a complex tool with 4 parameters and detailed output, this is highly 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?

The input schema has 100% description coverage, so the baseline is 3. The description does not add parameter-specific details beyond what the schema already provides, but it does reference the overall request count and endpoint checks, slightly reinforcing the check_endpoints/check_structured_data semantics. This is sufficient given the schema's thoroughness.

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 explicitly states the tool's function: 'Give it a domain and it returns whether an AI agent can read that site, and what the site's policy says'. It also enumerates the output families, providing a specific verb+resource+scope. Even without siblings, it is clearly differentiated from generic audit tools.

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 names the target user ('technical SEO or growth engineer', 'agency') and use case ('preparing a site for AI crawlers', 'before and after audit'). It also mentions prerequisites (APIFY_TOKEN, credits). However, it does not explicitly state when not to use the tool or mention alternatives, which is acceptable given there are no siblings.

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