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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds value by detailing the behavior: fetches the page, extracts title/description/key links, and emits standard llms.txt markdown format. This goes beyond the annotations and helps the agent understand the process and output.

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 and front-loaded with the main action. The first sentence conveys the core purpose and process, and the 'Useful for' list adds practical scenarios without unnecessary fluff. While slightly long, every sentence earns its place.

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?

For a simple tool with only 2 parameters and no output schema, the description provides sufficient context: what it does, how it works, and what output to expect. It does not explain error handling or edge cases, but that is not critical for this straightforward generation task.

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%, with both parameters (url, max_links) clearly described. The description adds little beyond the schema, only emphasizing 'any URL' and mentioning output format. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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: 'Generate a production-ready llms.txt file for any URL'. It uses a specific verb (generate) and resource (llms.txt file), and explains the process (fetches, extracts, emits). This distinguishes it from sibling tools like ai_visibility_check, which focus on checking visibility rather than generating files.

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 offers explicit 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.' It provides clear context for when to use the tool, though it does not mention specific alternatives or exclusions.

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