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

Annotations already cover read-only, idempotent, open-world, and non-destructive hints. The description adds behavioral details: it fetches the page, extracts title/description/key links, and outputs the standard llms.txt markdown format. This enriches the annotations without contradiction, though it does not mention error handling or rate limits.

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?

Three sentences, front-loaded with the main purpose, then process, then use cases. No fluff, every sentence provides value, and the structure is easy to scan.

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 simplicity, the description is reasonably complete: it explains the output format ('single text blob'), the process, and use cases. Without an output schema, it does not detail edge cases like unreachable URLs, but for a two-parameter read-only tool, it covers the essential context.

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 both url and max_links having clear descriptions. The tool description does not add additional parameter semantics beyond the schema, which is acceptable given the high coverage, so baseline 3 applies.

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 generates a production-ready llms.txt file for any URL, with a specific verb ('Generate') and resource ('llms.txt file'). It also outlines the process (fetches page, extracts content, emits markdown) and gives use cases, distinguishing it from sibling tools that focus on analysis or lookups.

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 provides clear contexts for when to use the tool via 'Useful for:' scenarios (client sites, own projects, auditing competitors) without explicitly naming alternatives or exclusions. It does not state when not to use it, but the use cases offer solid guidance.

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