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Check a page's structured data (schema.org) (paid)

check_structured_data
Read-onlyIdempotent

Check a page's structured data: finds every schema.org item (JSON-LD, microdata, RDFa), reports JSON-LD that does not parse, checks each item against the required and recommended properties for common types (LocalBusiness, Organization, Product, Offer, Article, FAQPage, BreadcrumbList, Event, Review, JobPosting and more), flags bad dates, ratings and URLs, and reports Open Graph, Twitter card and canonical tags, with a list of fixes. Costs 0.01 USDC per completed call, paid automatically via x402 if your client supports it. Never charged if the call fails.

Input Schema

NameRequiredDescriptionDefault
urlYesPublic http(s) URL of the page or website

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior5/5

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

Annotations already cover read-only/idempotent/open-world, yet the description adds behavior they cannot express: a concrete price (0.01 USDC), the payment mechanism (x402 auto-payment, so the client must support it), and the failure-billing policy ('never charged if the call fails'). That is exactly the kind of auth/cost context an agent needs before invoking a paid tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action, then a dense but purposeful clause listing checks and a final sentence on pricing. It is a single long sentence with little waste, though the feature enumeration is heavy enough that it could be broken up for scanability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

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 burden of describing results and does so ('reports JSON-LD that does not parse... with a list of fixes'). Combined with annotations covering the safety profile and the schema covering the one parameter, nothing material is missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is a single parameter whose schema description already covers it fully (100% coverage: 'Public http(s) URL of the page or website'). The description adds no format or constraint details beyond the schema, so the baseline 3 for a fully-documented single param applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a precise verb+resource ('Check a page's structured data') and then enumerates exactly what is inspected: JSON-LD/microdata/RDFa items, parse failures, required/recommended properties per type, bad dates/ratings/URLs, and Open Graph/Twitter/canonical tags. That scope is unmistakably distinct from siblings like check_tracking or audit_landing_page, so an agent can select it without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Applicability is strongly implied by the narrow scope (use it when you need to validate schema.org markup on a URL), but the description names no alternatives and gives no explicit when-not guidance relative to audit_landing_page or check_tracking. Usage is inferable rather than stated.

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

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