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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the agent knows it's a safe read. The description adds behavioral context (fetches page, extracts title/description/key links, emits markdown format) that goes beyond annotations, though it omits failure modes or rate limits.

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 front-loaded with the core purpose, immediately followed by a concise explanation of the process, output, and use cases. Every sentence adds value, with no redundancy or fluff.

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?

Despite lacking an output schema, the description clarifies the output format (llms.txt markdown, single text blob) and covers the simple input requirements. It provides sufficient context for the agent to understand the tool's behavior, though it could mention potential errors or limitations.

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%, so the schema already documents both parameters. The description does not add any parameter-level details beyond what is in the schema, so it meets the baseline expectation without exceeding it.

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 ('llms.txt file for any URL'), clearly states the output format, and lists concrete use cases. It distinguishes the tool's purpose from sibling tools by focusing on llms.txt generation, which is unique among the sibling list.

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 lists three explicit use cases (client site indexing, drafting for own project, auditing competitors), which guides when to use the tool. However, it does not mention when to avoid using it or suggest alternative tools (e.g., ai_visibility_check, scan_competitor_ai_presence) for related tasks.

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