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

The description explains the behavior: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This aligns with annotations (readOnlyHint, idempotentHint, destructiveHint false) and adds process details beyond what 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?

Three sentences: first sentence states purpose, second summarizes process and output, third lists use cases. No redundant or filler content; front-loaded with the most important information.

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 simplicity of the tool (2 params, no output schema, comprehensive annotations), the description covers all necessary aspects: what it does, how it works, output format ('single text blob ready to drop at site-root/llms.txt'), and use cases. Nothing essential is missing.

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 clear descriptions for both parameters. The description does not add new semantic information beyond what is already in the schema, meeting the baseline expectation.

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, specifying the verb, resource, and purpose. It distinguishes itself from sibling tools like ai_visibility_check and scan_competitor_ai_presence by focusing on creating a specific file format for AI crawlers.

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 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'). It does not explicitly state when not to use this tool or compare it to alternatives, but the given scenarios are clear.

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