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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description adds valuable behavioral context: it 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It also clarifies that the output is a single text blob ready to drop at site-root/llms.txt, which helps the agent understand what the tool actually does 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 concise, front-loaded with the main action, and every sentence adds value. It includes a clear one-line summary, a brief process explanation, and a 'Useful for' list—all in three sentences without unnecessary 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, the complete input schema, and the annotations, the description covers the essential aspects: what it does, how it works, the output format, and typical use cases. It explicitly states the output is a text blob, which is important since there is no output schema.

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 already covers both parameters fully (url and max_links with descriptions), so the description does not need to add much. It mentions 'key links' but does not explain max_links; however, the schema's own description handles this. Baseline of 3 is appropriate since the schema provides the heavy lifting.

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 with a specific verb and resource: 'Generate a production-ready llms.txt file for any URL.' It also names the output format and the intended audience (AI crawlers), which distinguishes it from sibling tools like ai_visibility_check that focus on checking 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 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'), making the intended context clear. It does not explicitly mention when not to use the tool or name alternatives, but the use cases are specific enough to guide selection.

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