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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description reveals the process: fetches the page, extracts title/description/key links, emits standard llms.txt markdown, and returns a single text blob. This provides useful behavioral context without contradicting the 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 compact and well-organized: first sentence states the core action, second explains the internal steps, third describes output, and final sentence lists use cases. Every sentence earns its place with no filler or redundancy.

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 no output schema, the description clarifies that the return is a text blob. It explains inputs (URL, optional max_links via schema), process, and output. It does not detail failure modes or limits (e.g., fetch timeouts), but given the simple nature and strong annotations, it is sufficiently complete for an agent to select and invoke correctly.

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 schema already describes both parameters (url and max_links) with 100% coverage. The description adds no new parameter-level detail, but it does mention 'title/description/key links' which aligns with what the tool extracts, indirectly supporting the url parameter. The baseline of 3 applies because schema handles semantics fully.

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 opens with a specific verb and resource: 'Generate a production-ready llms.txt file for any URL.' It clearly states the purpose (helping AI crawlers index a site) and distinguishes itself from siblings like ai_visibility_check or scan_competitor_ai_presence by focusing on file generation rather than analysis.

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'), which serve as clear when-to-use guidance. It does not mention when NOT to use or name alternatives, but the use cases cover typical scenarios.

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