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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by explaining the internal steps (fetches page, extracts title/description/links, emits markdown) and the output location (site-root/llms.txt). 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 a single paragraph that front-loads the main action. While it includes valuable details (use cases, output format), it could be slightly more concise. Overall, every sentence earns its place.

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?

Given the tool has only two parameters, no output schema, and comprehensive annotations, the description fully covers purpose, process, output format, and use cases. No significant gaps remain 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?

Both parameters are documented in the schema with descriptions. The tool description reinforces the url purpose and adds default and max values for max_links (default 25, max 50), which enhances understanding beyond the schema.

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: generate a production-ready llms.txt file for any URL. It specifies the output format (standard llms.txt markdown) and lists concrete use cases (getting a client's site indexed, drafting for own project, auditing competitor). This distinguishes it from siblings like ai_visibility_check or deep_research.

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 usage context via 'Useful for:' but does not explicitly state when not to use the tool or compare it to alternatives. However, since no sibling tool performs the same function, the implicit guidance is sufficient for decision-making.

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