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

Annotations already declare this as read-only, non-destructive, and idempotent. The description complements this by revealing the internal process: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It also discloses that output is a single text blob, adding context 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 three sentences: purpose, process, and use cases. It is front-loaded with the core action and contains no filler or redundant information. Every sentence adds value.

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 two parameters, no output schema, and strong annotations, the description covers all necessary aspects: what it does, how it works, what it returns, and when to use it. It is fully self-contained for the agent.

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 'url' and 'max_links' already clearly documented. The description does not add parameter-specific details beyond what the schema provides, so the baseline score of 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 opens with a specific verb-plus-resource: 'Generate a production-ready llms.txt file for any URL'. It clearly states the output format and names the intended consumers (AI crawlers like ChatGPT, Claude, Perplexity), distinguishing this tool from siblings focused on 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 'Useful for' list provides three concrete scenarios: getting a client's site indexed, drafting for your own project, and auditing a competitor's AI visibility. This gives clear contextual guidance, though it doesn't explicitly mention when not to use the tool or name alternatives.

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