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

Annotations already declare readOnlyHint=true and destructiveHint=false, establishing a safe read operation. The description adds process details (fetches the page, extracts title/description/key links) and mentions output format (standard llms.txt markdown), but it doesn't disclose edge behaviors like timeout handling or errors. With strong annotation coverage, a 3 is appropriate as the description adds some context beyond the structured data.

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, front-loaded with the core purpose, and every sentence adds value. It avoids fluff and repetition, efficiently covering purpose, process, output, and use cases in a compact format.

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 tool with 100% schema coverage, useful annotations, and no output schema needed (since output is a text blob), the description covers the essential context: what it does, how it works, and when to use it. It's slightly incomplete in terms of edge-case behavior, but it's well-suited to the tool's simplicity.

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%, so both parameters (url and max_links) are fully documented in the schema itself. The description does not add extra parameter-specific context, such as how max_links interacts with pagination. Baseline 3 is correct since the schema carries the heavy lifting and the description adds no extra semantic value.

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 opens with a specific verb ('Generate') and a concrete resource ('a production-ready llms.txt file for any URL'), clarifying both the action and the target. It distinguishes itself from sibling tools like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on producing the actual llms.txt markdown, not just analyzing visibility.

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 explicitly lists three practical use cases: getting a client's site indexed, drafting llms.txt for your own project, and auditing a competitor. While it doesn't state when to avoid this tool or mention alternatives by name, the use-case list provides clear context for when it is appropriate, earning a 4.

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