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

The description explains the tool's behavior: it fetches the page, extracts title/description/key links, and outputs standard llms.txt markdown. This adds context beyond the annotations (which already indicate read-only, idempotent, non-destructive behavior). The description aligns with the annotations without contradiction.

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, with no superfluous words. It immediately states the core purpose and follows with behavioral details and usage examples. Every sentence adds value, making it highly efficient for an AI agent to parse.

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 is fairly complete given the tool's complexity: it specifies input, process, output format, and use cases. However, it does not mention error handling (e.g., invalid URLs) or edge cases like pages without meta tags, which would add robustness. With no output schema, the description should ideally cover the output format more explicitly, though it does state 'single text blob ready to drop at site-root/llms.txt'.

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' and 'max_links') have clear descriptions in the schema. The tool description does not add any semantic information beyond what the schema already provides. The baseline score of 3 is appropriate since the description adds no extra value for parameter understanding.

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 an llms.txt file for a given URL, using specific verbs ('Generate', 'fetches', 'extracts', 'emits') and the resource 'llms.txt file'. It distinguishes itself from sibling tools by focusing exclusively on llms.txt generation, while other tools like ai_visibility_check or scan_competitor_ai_presence 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 description lists three concrete use cases (client indexing, own project drafting, competitor auditing) that clearly indicate when to use this tool. However, it does not explicitly mention when not to use it or name alternatives for related but different tasks, such as using ai_visibility_check for a simpler presence check.

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