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

Annotations already indicate readOnly, idempotent, and non-destructive. The description adds that it fetches the page, extracts title/description/key links, and returns markdown text. This aligns with annotations and provides behavioral context, though it could mention that no modifications are made to the site.

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 plus a brief list of use cases. It is front-loaded with the core action and every part adds value, with no redundant or superfluous content.

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 the tool's simplicity (2 parameters, no output schema), the description adequately covers functionality, output format, and use cases. It does not cover error handling or limitations (e.g., invalid URLs), but for a straightforward tool this is sufficient.

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 the schema already documents both parameters well. The description reinforces the meaning of 'url' (site to summarize) and 'max_links' (controls number of link entries) but does not add significant new information 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 states the tool generates a production-ready llms.txt file for any URL, specifying the action (fetch, extract, emit) and the output format. It distinguishes itself from sibling tools like 'ai_visibility_check' or 'scan_competitor_ai_presence' by focusing on llms.txt generation.

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 lists use cases: getting a client's site indexed, drafting llms.txt for own project, or auditing competitor AI visibility. It provides clear context but does not mention when not to use this tool or prerequisites like URL accessibility.

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