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

Annotations already cover the safety profile (readOnlyHint, openWorldHint, idempotentHint, destructiveHint). The description adds meaningful behavioral context beyond annotations, such as that it fetches a URL, extracts content, and emits standard llms.txt markdown, and that the output is a single text blob. It does not contradict the annotations and provides useful process details, though it omits potential edge cases like errors 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?

The description is a single paragraph of four sentences, front-loaded with the primary purpose, followed by process details and use cases. It is slightly verbose due to the use-case list, but every sentence contributes meaning. It could be tightened, but it is well-structured and not padded.

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), the description is quite complete. It explains the output format ('single text blob ready to drop at site-root/llms.txt') and includes use cases. It does not cover failure modes or auth requirements, but given the annotations and schema, the description provides enough context for an agent to invoke it 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 fully documents both parameters with descriptions (100% coverage), so the baseline is 3. The description adds little beyond the schema—it implies the URL is the target site and mentions the output format, but does not explain max_links semantics. The schema already handles parameter meaning adequately.

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's purpose with a specific verb and resource: 'Generate a production-ready llms.txt file for any URL.' It also explains the process (fetches page, extracts title/description/key links) and the output format. This distinguishes it from sibling tools like ai_visibility_check or scan_competitor_ai_presence, which focus on auditing AI presence rather than generating the file.

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 the agent context on when to use the tool, though it does not explicitly mention when not to use it or name alternative tools, falling short of the top score.

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