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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds meaningful process detail: it fetches the page, extracts title/description/key links, and emits a single text blob. This enriches the agent's understanding beyond the annotations, though it omits potential failure modes 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 front-loaded and compact, with the first sentence stating the core purpose and target users, and the second adding process and use cases without redundancy. Every sentence earns its place.

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?

For a simple two-parameter tool with strong annotations, the description covers purpose, process, output format, and use cases adequately. Minor omissions such as error handling for unreachable URLs are acceptable given the tool's simplicity.

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 describes both parameters (`url` and `max_links`) with examples and defaults, achieving 100% coverage. The description adds no extra parameter meaning, so the baseline 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 a given URL, with specific extraction and formatting details. It does not explicitly contrast with sibling tools like scan_competitor_ai_presence, so it stops short of full differentiation.

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 three concrete use cases: getting a client's site indexed, drafting llms.txt for a project, and auditing a competitor's AI visibility. This gives clear context for when to use the tool, but it does not provide exclusionary guidance or mention alternative tools.

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