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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context: it 'fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format,' disclosing network activity and output type. 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?

The description is concise and front-loaded with the main function, followed by behavioral details and use cases. Every sentence adds value, with no fluff or repetition. The structure is well-organized and easy to scan.

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 two-parameter tool with rich annotations and full schema coverage, the description is complete. It explains the output format ('single text blob'), the fetching behavior, and the use cases. No output schema exists, so the description adequately covers what the agent needs to know for correct invocation.

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 both url and max_links having clear descriptions. The description does not add parameter-specific detail beyond the schema, but it does mention 'key links' which aligns with max_links. Baseline of 3 applies since the schema fully handles parameter documentation.

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 purpose: 'Generate a production-ready llms.txt file for any URL.' It specifies the exact output (standard llms.txt markdown) and distinguishes itself from sibling tools by focusing on file generation rather than analysis or checking. The mention of AI crawlers (ChatGPT, Claude, Perplexity) adds context for its intended use.

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.' This gives strong contextual guidance on when to use the tool, though it does not name alternatives or exclusion criteria. The contrast with siblings like ai_visibility_check is implicit but not directly addressed.

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