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

The description discloses that the tool fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. This adds behavioral context beyond the readOnlyHint and idempotentHint annotations, though it does not discuss edge cases like handling large pages or failures.

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 are tightly packed: the first states the primary purpose, the second explains the process and output, and the third lists practical use cases. No filler or repetition, and key information is front-loaded.

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 simple 2-param tool with strong annotations, the description fully covers what it does, how it works, and what output to expect (a single text blob in standard llms.txt format). The use-case list adds practical context, making this 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?

Schema coverage is 100%, with both url and max_links having descriptive text. The description adds no additional parameter-specific meaning, 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 clearly states it generates a production-ready llms.txt file for any URL, with specific actions (fetches, extracts, emits) and distinct use cases (client indexing, personal project, competitor audit). This differentiates it from sibling tools that audit AI presence rather than generate files.

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?

It provides three concrete usage scenarios ('getting a client's site indexed', 'drafting llms.txt for your own project', 'auditing how an AI crawler would see a competitor'), giving clear context for when to use it. However, it does not explicitly name alternative tools or state when not to use it, 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.