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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that the tool fetches the page and extracts specific elements, which aligns with the read-only nature. It does not contradict annotations. It could mention that it does not modify the source, but the annotations already cover that.

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 two short sentences with zero waste. The first sentence covers what, how, and output. The second lists use cases. It is front-loaded and every phrase earns 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?

Given the low complexity (2 params, 100% schema coverage, no output schema), the description fully explains the tool's behavior, output format, and use cases. Annotations cover safety. No gaps for an AI 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 coverage is 100%, with clear descriptions for both 'url' and 'max_links'. The description reiterates the default and maximum for max_links but adds no new meaning beyond the schema. Baseline 3 is appropriate since the schema already does the heavy lifting.

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 verb 'generate', the resource 'llms.txt', and explains the process: fetches page, extracts title/description/key links, emits standard markdown. It also mentions AI crawlers and the single-text-blob output format. The purpose is distinct from sibling tools like 'scan_competitor_ai_presence' or 'deep_research', as none focus on llms.txt generation.

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: indexing a client's site, drafting for your own project, auditing competitors. It implies when to use by stating 'useful for'. However, it does not specify when not to use or mention alternative tools, which would elevate it to a 5.

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