Structured Data (Schema.org) Check

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

The description adds meaningful behavior beyond the readOnlyHint annotation: it explains that unparseable JSON-LD is invisible to search engines, that it checks missing required and recommended properties, and that it returns a score/grade. No contradictions with annotations.

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 well-structured with an action-focused first paragraph, a concise list of covered types, and a clear args/returns/example breakdown. It is appropriately sized and every section earns its place, though the type list is long but valuable.

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 2-param schema, rich output schema, and safe annotations, the description is complete for selection and invocation. It provides the full return structure, example usage, and supported types, leaving no critical gaps.

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?

Input schema covers 100% of parameters with descriptions, so baseline is 3. The description's Args section largely restates the schema (e.g., 'url (string): the page to check') without adding extra meaning beyond what the schema already provides.

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 clearly states it extracts and validates JSON-LD, microdata, and RDFa, and reports @type found, parse errors, and Google rich-result requirements. This specific verb+resource combination distinguishes it from sibling tools like render_check or seo_audit, which cover different aspects.

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 example ('Does https://example.com/product have valid Product schema?') gives a concrete use case, and the list of covered types implies when the tool applies. It does not explicitly name alternatives or exclusions, but the purpose is so specific that usage context is clear.

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