Check Structured Data

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

The readOnlyHint annotation already signals this is a safe read operation. The description adds meaningful behavioral scope by specifying exactly which structured data formats are handled and which JSON-LD types receive validation. However, it does not disclose return format, validation reporting style, or any limitations, which would have made it more transparent.

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 well-constructed sentence that front-loads the core action and resource, then packs the supported formats and validation types into a compact, readable list. There is no redundant wording or filler, and every clause contributes useful information.

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 one fully described parameter, a read-only annotation, and no output schema, the description explains the input and scope adequately. However, it omits what the agent should expect as output—raw extracted data, validation errors, a report, or a combination—which is important since there is no output schema to fill that gap.

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?

The schema covers 100% of the single parameter, url, with a description that clearly states it is 'The URL to extract structured data from'. The tool description adds no additional parameter-level meaning, so the schema carries the full burden. Baseline 3 is appropriate because the schema already does the job.

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 phrase, 'Extract and validate', and a clear resource, 'structured data from a web page'. It goes further by enumerating the exact formats covered (JSON-LD, Microdata, Open Graph, Twitter Card) and calling out type-specific validation for Product, Organization, BreadcrumbList, and Article. This makes the tool easily distinguishable from siblings like fetch_page or extract_content.

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 implies the tool should be used when structured data extraction or validation is needed, but it gives no explicit guidance on when to choose this tool over closely related siblings such as extract_with_schema or check_datalayer. There are no stated exclusions, prerequisites, or alternate tool recommendations.

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