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

Annotations cover the safety profile (readOnlyHint=true, openWorldHint=true), and the description goes further by disclosing the scoring model, the 5-of-20 vs 15-of-20 weighting for training vs fetch bots, the returned artifacts, and a latency estimate (5–15 seconds). This is meaningful context beyond the annotations, though it does not discuss rate limits or failure modes.

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 definition is front-loaded with what is checked, then the output, then the profile mechanic and a latency note. It is somewhat dense, but each sentence carries distinct information and none is redundant.

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?

There is no output schema, so the description correctly compensates by naming the return values (score out of 100, per-check points, actions, AI-open assets). Combined with a full-coverage input schema and safety annotations, an agent has enough to call it correctly; only deeper error/timeout behavior is absent.

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%, so the schema already documents both parameters. The description still adds value by explaining the 'yardstick' concept behind profile and clarifying that it is auto-detected from the site when omitted, which is more than the schema states.

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 states a specific verb (checks) and resource (a public website's AI-readiness) and enumerates exactly what is inspected: robots.txt, sitemap, schema.org, llms.txt, ai-catalog.json and prompt-injection text. This is clearly distinct from the data-lookup siblings (check_business, find_business), so an agent can route correctly without opening the schema.

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?

Usage is only implied by the purpose ('checks how a public website looks to AI assistants'). There is no explicit when-to-use, when-not-to-use, or comparison against any sibling tool, so the agent must infer that this is for auditing a site rather than looking up entity data.

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