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

Annotations indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds value by explaining the internal process (fetches page, extracts title/description/key links, emits markdown) and the output format (single text blob). No contradictions with 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?

Three sentences cover purpose, process, and use cases. Front-loaded with the main action ('Generate a production-ready llms.txt file'). No unnecessary words; 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?

Given two simple parameters, no output schema, and clear annotations, the description fully covers what the tool does, how it works, and what the output looks like. No gaps remain for an agent to configure or invoke it 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 descriptions for both parameters (url, max_links). The description adds context beyond schema by explaining that the output is a text blob ready for site-root/llms.txt and how the tool processes the URL. This enriches meaning beyond raw parameter names.

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 generates an llms.txt file for a URL, specifying the action (generate), resource (llms.txt file), and scope (for any URL). It distinguishes from siblings like scan_competitor_ai_presence and ai_visibility_check by focusing specifically on producing the standardized llms.txt format, making it unique.

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, drafting for own project, auditing competitor indexing. This gives clear context for when to use. However, it does not explicitly state when not to use or list alternative tools, missing full exclusion criteria.

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