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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses the tool's behavior: it fetches the page, extracts key fields, and produces a markdown output. This adds valuable process context without contradicting 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, front-loads the core purpose, and then adds output details and use cases. Every sentence contributes value, with no filler or unnecessary 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 tool with no output schema, the description clearly states the return format ('single text blob') and covers process, use cases, and safety profile through annotations. It is sufficiently complete for an agent to understand what the tool does and when to invoke it.

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% for both parameters, so the baseline is 3. The description does not add meaningful parameter semantics beyond what the schema already provides; it only subtly aligns 'any URL' with the url parameter and 'key links' with max_links.

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 a clear resource ('llms.txt for any URL'), and goes on to explain the process: fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. This distinctly positions the tool against siblings such as ai_visibility_check or scan_competitor_ai_presence, which serve different purposes.

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:' section gives concrete scenarios like getting a client's site indexed by AI, drafting llms.txt for a project, or auditing competitor visibility. This provides clear context on when to use the tool, but does not explicitly name alternatives or state when not to use it, so it falls short of a 5.

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