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

The description adds behavioral context beyond the annotations by explaining that it 'fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format' and that output is a 'single text blob ready to drop at site-root/llms.txt.' This clarifies the tool's read-only, idempotent nature, consistent with the annotations. It does not cover edge cases like authentication or rate limits, but the annotations already signal safety.

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 concise (three sentences), front-loaded with the primary purpose, followed by process details and use cases. Every sentence adds value without unnecessary fluff, making it well-structured and easy to digest.

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 description is complete for this tool's simplicity. It covers the purpose, process, output format, and practical use cases. With good annotations and a fully described schema, the description provides all needed context for an AI agent to select and invoke the tool correctly. No output schema exists, but the description explicitly states the return is a text blob, which is sufficient.

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?

The input schema already describes both parameters (url and max_links) with clear descriptions, so coverage is 100%. The tool description does not add extra meaning to the parameters; it only implies the url by 'any URL' and mentions 'key links' without discussing max_links. Baseline 3 is appropriate since the schema 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 tool's function: generating a production-ready llms.txt file for any URL. It uses a specific verb ('Generate') and resource ('llms.txt'), and details the process (fetches the page, extracts title/description/key links, emits standard markdown), making its purpose unambiguous and distinct from sibling tools.

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 usage scenarios: '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.' This gives clear when-to-use context, though it does not explicitly mention alternatives or exclusions, so it falls short of a 5.

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