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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it fetches the page, extracts title/description/key links, and emits a 'single text blob ready to drop at site-root/llms.txt.' This goes beyond the annotations by explaining the internal pipeline and output format, though it does not discuss error handling or network failures.

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 long, front-loaded with the core purpose, and every clause adds value. It efficiently covers what the tool does, how it does it, the output format, and concrete use cases without any fluff or repetition.

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 no output schema, the description sufficiently explains the output format and use context. It covers the main workflow and expected deliverables. It lacks mention of potential failure modes (e.g., unreachable URLs) or prerequisites, but given the tool's simplicity and the strong annotations, it is reasonably complete.

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 description coverage is 100%, and both parameters (url, max_links) are already well-described in the schema, including default and max values for max_links. The description adds minimal semantic value for parameters, only reinforcing that the URL can be any site or landing page. This meets the baseline for high schema coverage.

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 specific action: 'Generate a production-ready llms.txt file for any URL' and follows with the concrete steps of fetching the page, extracting title/description/key links, and emitting standard llms.txt markdown. It distinguishes this tool from siblings like ai_visibility_check or scan_competitor_ai_presence by focusing on file generation rather than merely checking visibility or scanning 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 gives explicit use cases: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor. This clearly indicates 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.