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

Annotations already convey safe, read-only, idempotent behavior. The description adds rich behavioral detail: 'fetches the page, extracts title/description/key links, and emits standard llms.txt markdown format.' It also specifies output as a single text blob. No contradictions.

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 compact (two sentences plus a bulleted use-case list). It front-loads the core action and output format. Minor improvement: could list parameters more explicitly, but overall efficient.

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 no output schema, the description fully covers the output format ('standard llms.txt markdown format', 'single text blob'). It also explains the process and gives use-case context, making it complete for a simple read-only tool.

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 description does not need to add parameter details. It merely rephrases the url and max_links purpose without deeper semantics. Baseline 3 is appropriate.

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's purpose: to generate a llms.txt file for any URL for AI crawlers. It uses specific verbs ('generate', 'fetches', 'extracts', 'emits') and distinguishes itself from general AI visibility tools by focusing on the llms.txt standard.

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 three use cases (getting client's site indexed, drafting for own project, auditing competitor). It does not provide when-not-to-use or compare with siblings like 'scan_competitor_ai_presence', but the context is clear enough for an agent to select it appropriately.

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