Benchmark vs industry/geo

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

Annotations declare readOnlyHint and openWorldHint, but the description adds critical context beyond them: the Website Auditor subscription requirement, free-trial details, and the fallback workflow to get_sample_audit. This is valuable behavioral disclosure not present in structured metadata, though it doesn't detail return format or error handling.

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 front-loaded with the core purpose and usage guidance. The final sentence about subscription and trial is long but information-dense, covering a prerequisite and an alternative tool. It could be split for readability but every clause earns its place.

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?

No output schema exists, but the description hints at the return type ('percentile/peer context') and references get_sample_audit's output format as a proxy. It also covers prerequisites and alternatives, making it reasonably complete for a read-only tool with three well-documented parameters.

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 description coverage is 100% for all three parameters (domain, geo, industry), so the description doesn't need to explain them. The description mentions 'industry and location' but doesn't add semantics beyond what the schema already provides, matching the baseline for high coverage.

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 opens with a specific verb ('benchmark') and resource ('website's AI visibility') against industry and location. It explicitly contrasts with absolute scoring ('percentile/peer context rather than an absolute number'), distinguishing it from sibling tools like get_ai_visibility and compare_competitors.

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 when-to-use guidance with example user queries and states the condition for using an alternative (call get_sample_audit when no subscription). It also implicitly excludes absolute-number requests, giving clear decision support.

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