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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds valuable process detail: it fetches the page, extracts content, and emits a standard llms.txt markdown blob. It also clarifies the output is a single text blob ready for placement, which is useful beyond 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 three sentences with a clear front-loaded purpose, a brief process breakdown, and a 'Useful for' list. No redundant phrasing; every sentence contributes to understanding what the tool does and when to use it.

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 two parameters, no output schema, and annotations covering read-only/idempotent behavior, the description provides sufficient context: input (any URL), process (fetch/extract), output (standard llms.txt markdown), and use cases. It is complete for an agent to select and invoke the tool effectively.

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% (both 'url' and 'max_links' are described in the input schema). The description does not add extra meaning about the parameters beyond what the schema already provides, 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 uses a specific verb ('Generate') and names the exact deliverable ('production-ready llms.txt file for any URL'). It clearly distinguishes this tool from siblings by focusing on the llms.txt generation workflow, including fetching the page, extracting title/description/key links, and emitting standard markdown.

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, such as getting a client's site indexed, drafting llms.txt for your own project, and auditing competitor AI visibility. However, it does not explicitly state when not to use it or name alternative tools, so it stops short of full exclusion guidance.

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