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

The description explains the process (fetches page, extracts title/description/key links, emits markdown) and the output format. Annotations already declare readOnlyHint and idempotentHint, so the description adds value by detailing the extraction behavior without contradicting 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 a single, well-organized paragraph with no redundant sentences. It front-loads the core action and output, then provides use cases efficiently.

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?

Despite no output schema, the description specifies the output as 'a single text blob ready to drop at site-root/llms.txt', which is sufficient. The tool's behavior is fully explained given its simplicity.

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 describes both parameters fully. The description adds contextual detail: default and maximum for max_links, and clarifies the url parameter scope. This goes beyond the schema alone.

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. It specifies the verb 'generate', the resource (URL), and the output format. It distinguishes itself from siblings by focusing on AI crawlers and llms.txt creation.

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 explicit use cases: getting a client's site indexed, drafting for own project, or auditing competitor. Although it does not explicitly state when not to use, the use cases provide clear guidance on applicability.

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