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

Annotations mark this non-read-only, open-world, non-idempotent and non-destructive; the description substantiates that by disclosing a live external crawl, a 1-credit charge, and an auth requirement — cost and authentication are exactly the operational facts annotations cannot convey. It stops short of saying whether results are persisted for later retrieval via get_audit or how long a crawl takes.

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?

Two sentences, front-loaded with the action and ending with the operational constraints (credit cost, sign-in). The parenthetical list of four AI engines is mild padding but does clarify the scoring perspective, so nothing is wasted.

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?

With no output schema, the description carries the return-value burden itself and does so: score, grade, category scores, prioritized fixes, shareable report link. Combined with cost and auth disclosure, an agent has everything needed to decide and invoke correctly for a single-parameter tool.

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?

Only one parameter and the schema already documents it at 100% coverage, including the bare-domain example. The description adds nothing about the URL parameter, so the baseline of 3 applies.

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?

States a specific verb and resource ('crawl a public website live and score how well AI search engines can access and read it') and enumerates the outputs. An agent knows exactly what it produces, though it never names the sibling it competes with — quick_check — so the boundary between a full audit and a quick check is left implicit.

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?

Gives prerequisites ('requires signing in with LLMready Pro') and a cost signal ('costs 1 credit'), which shape the decision to call it. But there is no explicit when-to-use guidance versus quick_check or get_audit, and no statement about when not to use it, so routing is inferred rather than stated.

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