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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds meaningful process details: it fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. It also clarifies the output as a single text blob intended for site-root/llms.txt. There is no contradiction with annotations, and the added context goes beyond what the annotations provide.

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 three sentences long, with the main action front-loaded. Each sentence adds distinct value: what it does, how it works, and when to use it. There is no redundancy or filler, making it appropriately concise for the information it conveys.

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?

This is a simple tool with two parameters and no output schema, so the description carries the responsibility of explaining behavior and results. It covers the purpose, process, output format, and use cases comprehensively. The annotations handle safety and mutability. No significant gaps remain for an agent to select and invoke the tool correctly.

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 covers both parameters (url and max_links) fully with descriptions, so the baseline is 3. The description does not add any additional meaning for max_links, but it reinforces that url is a full URL by saying 'any URL' and mentioning site-root placement. Since schema coverage is 100%, the description is not required to compensate.

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 verb and resource: 'Generate a production-ready llms.txt file for any URL'. It goes beyond a simple statement by detailing the process (fetches, extracts, emits) and the output format, making it easy for an agent to understand exactly what the tool does. The 'Useful for' list clarifies distinct use cases, distinguishing it from any potential sibling that might audit AI visibility or scan competitor presence.

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 a 'Useful for' section with three explicit scenarios, giving clear context on when to use this tool. It does not explicitly name alternatives or provide when-not-to-use guidance, so it falls short of a 5, but the use cases and the 'any URL' scope make the conditions for use reasonably explicit.

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