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

The description explains the internal process: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It also discloses the output format ('single text blob') and placement. Since annotations already declare readOnly/idempotent safety, this additional context is valuable and not redundant.

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 compact and front-loaded, with two sentences covering the core functionality and a brief, focused 'Useful for' list. Every statement contributes useful information without fluff.

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 (2 params, no output schema) and solid annotations, the description covers purpose, process, output format, and use cases. It is sufficiently complete 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?

Both parameters are fully described in the schema, including the default and max for max_links. The description does not add extra meaning beyond what's already in the schema, so the baseline score of 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 specific verb 'Generate' and resource 'llms.txt'. It also explains the purpose (helping AI crawlers index) and distinguishes from sibling tools that audit AI presence or visibility.

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 lists concrete 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.' However, it does not explicitly mention alternative sibling tools or when not to use this tool, so it falls just short of full guidance.

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