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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by detailing the internal steps: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This gives the agent a clear mental model of what happens, beyond what annotations provide. It does not mention edge cases or failure modes, but the annotations lower the burden.

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 long and highly efficient. It front-loads the main action, then explains the process, and ends with practical use cases. Every sentence adds value with no redundancy or filler. The structure is ideal for quick comprehension by an AI agent.

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 two-parameter tool with no output schema, the description covers the essential context: what it does, how it works, what the output looks like, and when to use it. Annotations fill in the safety profile. It lacks explicit error-handling details or rate-limit information, but those are not critical for this kind of read-only, idempotent tool. It is complete enough for most use cases.

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 covers both parameters with clear descriptions (url as the site to summarize, max_links with default and maximum). Since schema description coverage is 100%, the description's role is minimal. The description does add context about the output ('single text blob ready to drop at site-root/llms.txt'), but it does not add significantly to the parameter meanings. Baseline 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 clearly states the tool's core function: generating a production-ready llms.txt file for any URL. It specifies the verb 'Generate', the resource 'llms.txt', and the scope 'for any URL', distinguishing it from sibling tools like ai_visibility_check or scan_competitor_ai_presence. The use cases further clarify its unique purpose.

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, auditing how an AI crawler would see a competitor'), which clearly imply when to use the tool. However, it does not explicitly mention when not to use it or name alternative tools, so it falls just short of a 5.

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