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

The description goes beyond the annotations by disclosing the internal behavior: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It also clarifies the output form ('single text blob'). The annotations already indicate a read-only, idempotent, non-destructive operation, and the description adds value without contradiction.

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 four sentences, each adding substantive value: what it does, how it works, what the output is, and when to use it. It is front-loaded with the core purpose and contains no filler or repetition.

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?

With only two parameters, no output schema, and rich annotations, the description covers all necessary context. It explains the output format, the use cases, and the internal process, making the tool fully understandable for an AI agent deciding whether and how to invoke it.

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 has 100% coverage for both parameters ('url' and 'max_links'), so the schema already explains them fully. The description does not add additional parameter-level semantics, only references 'key links' which aligns with the schema. Baseline 3 is appropriate given the high 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 uses a specific verb ('Generate') and resource ('production-ready llms.txt file for any URL'), and clearly explains the process and output format. It also distinguishes itself from siblings by focusing on generating the file rather than scanning or checking AI visibility, which is the theme of several 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 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') and makes it clear when the tool is applicable. However, it doesn't explicitly mention when not to use the tool or name alternative sibling tools, so it's not a full when/when-not/alternatives guidance.

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