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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses the tool's internal behavior: it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. This adds meaningful procedural context without contradicting the annotations.

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—two sentences—with the primary action front-loaded. The 'Useful for' clause adds practical context without redundancy. Every sentence earns its place, and the structure is easy to scan.

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 tool with two well-documented parameters and no output schema, the description sufficiently explains the process, output format, and primary use cases. It could mention potential error conditions (e.g., unreachable URL) but is otherwise complete enough 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 already fully described in the input schema (url and max_links), so schema coverage is 100%. The description reinforces that the URL can be any site and that the output is a text blob, but it does not add significant semantic detail beyond the schema for the parameters.

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 opens with a specific verb+resource pairing: 'Generate a production-ready llms.txt file for any URL.' It clearly explains the tool's core function, including what it fetches, extracts, and emits, and differentiates it from sibling tools by focusing specifically on llms.txt generation rather than broader AI visibility scanning.

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 'Useful for' scenarios: getting a client's site indexed, drafting llms.txt for a project, and auditing a competitor's AI-crawler view. This gives clear context for when to use the tool, though it does not explicitly name alternatives or state when not to use it.

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