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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral details: it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown format. This explains the process without contradicting annotations. No side effects are mentioned beyond fetching.

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 long, front-loading the core purpose and audience, then briefly explaining the process, and finally listing use cases. Every sentence adds value, with no redundancy or fluff.

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?

The description explains the output format ('single text blob ready to drop at site-root/llms.txt') and the process (fetch, extract, emit). It lacks details on error handling or edge cases (e.g., invalid URL), but for a simple generation tool with good annotations, it is largely 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%, so baseline is 3. The tool description does not add any parameter-level meaning beyond what the schema already provides (e.g., url as full URL, max_links with defaults). No new semantics are introduced.

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 resource ('llms.txt file') and clearly states the tool's purpose: to create a production-ready llms.txt for any URL so AI crawlers can index the site. It also lists concrete use cases (e.g., getting a client's site indexed, drafting for own project, auditing competitors), which distinguishes it from sibling tools like ai_visibility_check or scan_competitor_ai_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 explicitly states when to use the tool via the 'Useful for' list, covering scenarios like indexing a client's site, drafting for a project, or auditing competitors. It does not explicitly state when not to use it, but the context is clear enough for an AI agent to infer appropriate usage.

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