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 process details: fetches the page, extracts title/description/key links, and emits standard format. This goes beyond annotations by explaining the workflow and output format.

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?

Three sentences: first states purpose and audience, second explains process, third lists use cases. No redundant words, front-loaded with key information. 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 no output schema, the description explicitly states output is a 'single text blob ready to drop at site-root/llms.txt'. Parameter count is low (2) with full schema coverage. The tool is simple and the description covers all necessary information for an agent to use it 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?

Input schema covers 100% of parameters with descriptions. The description clarifies the overall purpose ('production-ready', 'standard format') but does not add significant new meaning to individual parameters beyond what the schema provides. Baseline 3 is appropriate.

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 generates a production-ready llms.txt file for any URL, with specific mention of target AI crawlers. It distinguishes itself from siblings like 'scan_competitor_ai_presence' by focusing on generating the standard llms.txt markdown document.

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: getting a client's site indexed, drafting for own project, or auditing competitor's AI crawler view. While it doesn't explicitly state when not to use it or name alternatives, the context is clear and actionable.

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