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

Annotations already declare readOnlyHint and idempotentHint, and the description meaningfully adds process details: it fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format as a single text blob. This discloses behavior well beyond the structured annotations, covering what the tool does and what the output looks like.

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 well-structured sentences plus a concise 'Useful for' list. Every sentence provides distinct value: what it does, how it works, and when to use it. No fluff or redundancy.

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?

For a tool with only 2 parameters and no output schema, the description fully covers the purpose, process, output format, and common use cases. It leaves no significant gaps for an agent to understand when and how to use it.

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 description coverage is 100%, so the schema fully documents both parameters. The description adds little new meaning beyond stating 'any URL' and implicitly the link extraction, so the 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 explicitly states it 'Generate a production-ready llms.txt file' with a specific verb and resource. It details the process (fetches page, extracts title/description/key links, emits standard markdown) and distinguishes it from sibling tools like ai_visibility_check or scan_competitor_ai_presence through the focus on generating the file itself.

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 use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' This gives strong contextual guidance, but it does not explicitly mention when not to use or directly compare to alternative tools, so it falls short of a 5.

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