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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety is covered. The description adds valuable behavioral context: it fetches the page, extracts specific elements, and outputs a 'standard llms.txt markdown format' ready for deployment. This goes beyond the annotations without contradicting them.

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 three sentences, efficiently front-loaded with the main purpose and followed by process, output, and use cases. Every sentence adds value with no fluff. It earns a perfect score for conciseness and structure.

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's simplicity (2 params, no output schema), the description is complete: it explains what it does, how it works, what output to expect, and when to use it. The annotations cover safety and idempotency, so the description covers all gaps and is fully self-contained for an agent to 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?

Schema coverage is 100%, with the schema already describing 'url' and 'max_links' in detail. The description adds only general context (e.g., 'for any URL') but doesn't provide additional parameter semantics beyond what the schema offers. 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 clearly states what the tool does: 'Generate a production-ready llms.txt file for any URL' and explains the process (fetches page, extracts title/description/key links, emits standard markdown). It distinguishes itself from sibling tools like ai_visibility_check or scan_competitor_ai_presence by focusing specifically on generating llms.txt, and it lists explicit use cases.

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 clear context for when to use the tool: '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.' It does not explicitly state when not to use it or mention alternative tools, but the use cases are specific enough to guide an agent.

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