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

Without annotations, the description must fully disclose behavior. It enumerates exactly which files and endpoints are checked and what is parsed (e.g., AI-bot rules from robots.txt). This provides high transparency, though it omits any potential side effects or return format details.

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 a single, dense sentence that efficiently lists all probed endpoints. It is clear and front-loaded with the core action, but could benefit from slight restructuring for readability.

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 complexity of probing multiple signals and lacking an output schema, the description adequately lists what is checked but does not describe the output format or structure of results. This leaves some ambiguity for the agent.

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?

With 100% schema description coverage, the schema already documents the domain parameter. The description adds context by clarifying it accepts 'example.com' or full URLs, but this is minor. The baseline score 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 probes a domain root for specific AI-discovery signals, listing files and endpoints like /llms.txt, /robots.txt, /sitemap.xml, and well-known resources. This distinguishes it from siblings like analyze_page which likely targets individual pages.

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 implies usage for site-wide AI discovery but does not explicitly state when to use versus alternatives. No exclusions or comparisons to sibling tools are provided, leaving the agent to infer context.

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