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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context beyond annotations by describing the fetch/extract/emit process and the output as a standard llms.txt markdown blob. This goes beyond the baseline, though it doesn't cover edge cases like invalid URLs.

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, front-loads the core purpose and process, and follows with concrete 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?

Given the tool's simplicity (2 parameters, no output schema), the description covers the necessary context: the output format is explicitly stated, the placement (site-root/llms.txt) is mentioned, and use cases are listed. The annotations handle safety and idempotency, so the description is complete for practical selection and invocation.

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?

The input schema provides 100% description coverage for both parameters (url and max_links), including defaults and format. The tool description does not add any extra parameter-level semantics beyond what the schema already states, so it 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 tool generates a production-ready llms.txt file for any URL, explaining the process (fetches, extracts, emits) and the output format. This distinguishes it from sibling tools like ai_visibility_check or scan_competitor_ai_presence, which focus on assessing AI presence rather than generating the file.

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 lists specific scenarios (indexing a client's site, drafting for own project, auditing competitors), providing clear context on when to use the tool. However, it does not explicitly mention alternatives or when not to use it, so it falls short of full exclusionary guidance.

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