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

Beyond the annotations (read-only, open-world, idempotent), the description adds process details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format' and notes the output is a 'single text blob ready to drop at site-root/llms.txt'. This gives useful behavioral context without contradicting any 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?

Three sentences with zero filler: the first states the core purpose, the second explains the process, and the third lists use cases. The most important information is front-loaded, making it easy for an agent to quickly determine relevance.

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 params, no output schema), the description is complete. It explains the return format ('single text blob ready to drop at site-root/llms.txt') and the overall behavior. The annotations cover safety aspects, so nothing major is missing.

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 schema already provides 100% parameter descriptions, so the baseline is 3. The description reinforces the 'url' parameter with 'for any URL' but does not add details about 'max_links' or default values beyond what the schema documents. No extra semantic value is provided.

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, with explicit reference to AI crawlers and the standard markdown format. It distinguishes itself from sibling tools by focusing on the specific llms.txt generation task rather than broader AI visibility checks or research.

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 provides concrete use cases ('getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor') giving clear context for when to use the tool. However, it does not explicitly mention alternatives or exclusions, though the 'competitor audit' use case hints at overlap with sibling tools like scan_competitor_ai_presence.

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