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

The description discloses the tool's behavior: it fetches the page, extracts title/description/key links, and emits a single text blob in standard llms.txt markdown. This adds process context beyond the readOnlyHint and idempotentHint annotations, confirming it performs a live read operation without mutation.

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 compact and front-loaded, stating the core purpose in the first sentence. The 'Useful for' list adds value without redundancy, making every sentence earn 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?

The tool is simple with only two parameters, both well-documented in the schema. Annotations cover safety and idempotence. The description specifies the output format ('single text blob') and use cases, making it complete for an agent to select and invoke correctly.

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% for both parameters (url and max_links), so the schema already fully documents them. The description doesn't add any additional parameter-specific details, so 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 function: generating a production-ready llms.txt file for any URL. It explains the process (fetches, extracts, emits) and distinguishes itself from sibling tools by focusing on file generation rather than visibility analysis.

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' section provides concrete scenarios such as getting a client's site indexed, drafting llms.txt for one's own project, or auditing a competitor's AI visibility. It doesn't explicitly name alternative tools or when not to use, but the context is clear.

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