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

The description discloses the underlying operation ('Fetches the page, extracts title/description/key links') and the output format, adding behavioral context beyond the annotations. It mentions a network fetch, which is a side effect, though annotations already indicate read-only, open-world, and idempotent behavior. This adds value 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 well-structured sentences: purpose, process, output/use cases. It is front-loaded and every clause contributes directly to understanding the tool, with no padding.

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 two-parameter tool with rich annotations and a full schema, the description is complete: it states the input URL, the optional link cap (via schema), the process, and the output format ('single text blob ready to drop at site-root/llms.txt'). No output schema exists, but the description adequately covers expected return.

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?

Both parameters are fully described in the schema (url and max_links), so the description doesn't need to add parameter-level detail. The description's mention of 'emits the standard llms.txt markdown format' partially implies the role of max_links but doesn't enhance beyond schema. Baseline 3 applies given 100% schema coverage.

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 with a specific verb ('Generate'), the resource ('llms.txt file for any URL'), and the intended audience ('AI crawlers (ChatGPT, Claude, Perplexity)'). It also outlines the process briefly, distinguishing it from sibling tools that audit AI presence rather than producing the file.

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 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'), giving clear context for when to apply it. However, it doesn't directly contrast with sibling tools like 'scan_competitor_ai_presence' or state when not to use it, so it lacks explicit exclusions.

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