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

The description adds value beyond annotations by explaining the fetch-extract-emit process and specifying the output format (a single text blob). It also notes the target location (site-root/llms.txt). Annotations already cover safety, and the description does not contradict them.

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 two sentences plus a use-case list, with the primary action front-loaded. Each sentence adds distinct information—function, process, output, and scenarios—without redundancy.

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 two-parameter tool with no output schema, the description sufficiently covers purpose, process, output, and usage contexts. The annotations provide safety clues, so the tool is fully contextualized.

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%, so the schema already documents both parameters. The description reinforces the URL parameter's role ('for any URL') and mentions 'key links' which relates to max_links, but doesn't add substantial new semantics beyond the schema.

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 specifies the tool's function: generating a production-ready llms.txt file for any URL. It distinguishes itself from sibling tools by emphasizing the concrete output (llms.txt file) rather than just checking AI presence. The explicit use cases further clarify scope.

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 'Useful for' list provides three concrete scenarios: obtaining a client's site indexed, drafting for your own project, and auditing competitors. While it doesn't name alternative tools explicitly, the scenarios effectively communicate when this tool is appropriate given sibling tools like scan_competitor_ai_presence.

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