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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is clear. Description adds context about fetching and extracting content but does not address rate limits or output size constraints; a 3 is appropriate as annotations carry much of the burden.

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 that front-load the purpose and provide immediate clarity. Every sentence adds value—no fluff, no repetition of schema. Perfectly sized for an AI agent to parse quickly.

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 read-only, idempotent, non-destructive annotations and the absence of an output schema, the description sufficiently explains what the tool returns (a text blob) and its use cases. No gaps remain for an agent to understand 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?

Schema coverage is 100% with clear parameter descriptions. Description implies the 'url' parameter is required and mentions 'max number of link entries' indirectly via 'max_links', but does not add syntax or formatting details beyond the schema. Baseline 3 is correct.

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 it generates a production-ready llms.txt file for any URL, detailing the process (fetches page, extracts title/description/key links, emits standard markdown). This specific verb+resource combination distinguishes it from sibling tools which are mostly about searching, betting, or entity profiles.

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?

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.' While it does not say when not to use, the sibling list makes the tool's niche clear.

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