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

Description explains the process: fetches page, extracts title/description/key links, emits standard markdown. Annotations indicate read-only, idempotent, non-destructive behavior, and description aligns without contradiction. Adds useful behavioral context beyond 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?

Description is three concise sentences front-loaded with the core purpose. Every sentence adds value: purpose, process, use cases. No fluff 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?

For a simple tool with 2 parameters and no output schema, the description adequately covers input, process, and output format. It could include an example output snippet, but the known standard format and explicit use cases make it sufficiently complete.

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 clear parameter descriptions. The main description echoes the 'url' purpose but does not add new semantic info for 'max_links'. Baseline score of 3 is appropriate as the schema already clearly defines parameters.

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?

Description clearly states the tool generates a 'production-ready llms.txt file' for any URL, specifying the verb, resource, and scope. It distinguishes from sibling tools like 'ai_visibility_check' and '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?

Explicitly lists use cases: indexing a client's site, drafting llms.txt, auditing competitor visibility. While no direct 'when not to use' or alternative mentions, the use cases provide clear guidance. Siblings offer related but distinct functions.

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