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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive hints. The description adds behavioral details: fetches the page, extracts title/description/key links, and emits standard llms.txt markdown format. It aligns with annotations and provides added context on the process.

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 concise, front-loaded with the core function, and includes use case examples. It is a single paragraph with no wasted words, though it could be structured with bullet points for clearer separation of use cases.

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 fully covers input, process, output format, and use cases. It mentions the output is a text blob ready to drop at site-root/llms.txt, providing complete context for an AI agent.

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 detailed descriptions for both parameters. The description does not add significant meaning beyond what the schema already provides. It repeats the default and max for max_links, but no new semantic insights.

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, targeting AI crawlers. It specifies the verb 'generate', the resource 'llms.txt file', and the context of indexing by AI crawlers. This distinguishes it from siblings like 'ai_visibility_check' or 'scan_competitor_ai_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 explicit use cases: getting a client's site indexed, drafting for own project, or auditing competitors. It gives clear context for when to use the tool, but does not explicitly state when not to use or mention alternatives.

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