RankRoot: AI visibility audit

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds genuinely useful return-shape context: a 0-100 score, per-category scores, and a prioritized fix list, which is not derivable from annotations or the input schema.

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, zero waste, and the core purpose is front-loaded ahead of the return-value summary.

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 single-parameter, read-only analysis tool with no output schema, the description supplies what an agent needs: what it computes, the target engines, and the shape of the result. Nothing material is missing.

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 with an example URL. The description adds no format or validation detail beyond the schema, so baseline 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 (score) and resource (website discoverability by AI answer engines), and enumerates the engines and agents in scope. An agent can tell this apart from the sibling generate_ai_visibility_files, which produces files rather than a score.

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 implied by the purpose (use it to audit AI visibility) but there is no explicit when-to-use, when-not-to-use, or reference to the sibling alternative. Adequate but leaves routing to inference.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety and external-access profile. The description adds useful specifics about which files are generated and that generation is based on the current homepage, but it does not clarify whether the files are returned as content or written somewhere, nor does it mention auth or rate-limit behavior.

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 description is a single front-loaded sentence with no wasted words, starting with the action and immediately listing the concrete output artifacts.

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 should carry more of the return-value burden. It names the generated file types but does not say how they are returned or packaged, nor does it note any prerequisite about homepage availability or authentication.

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 single url parameter is already documented in the schema. The description adds only marginal meaning by implying the URL should point to the website's current homepage; it does not add format, protocol, or validation details beyond the schema.

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 (Generate) and concrete resources (llms.txt, AI-bot-friendly robots.txt, schema.org JSON-LD) for a website. It distinguishes the action from the sibling audit_ai_visibility only implicitly through the word 'generate'; it never names the sibling or explicitly contrasts generation with auditing.

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 gives no explicit when-to-use guidance, no exclusions, and no mention of the sibling audit_ai_visibility. An agent can infer it is used when those files are needed, but there is no routing logic or condition selection.

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