Free Schema Markup Audit Tool

Structured data is not a set-and-forget implementation. Schemas drift out of date, new schema types become available, Google's rich result requirements change, and the rise of AI search engines creates entirely new optimization opportunities. A comprehensive audit catches issues that simple validation misses — like schema types that should exist on a page but don't, or fields that technically pass validation but are too sparse to generate rich results or AI citations.

What This Audit Checks

Our Schema Audit Tool goes far beyond basic JSON-LD validation. It performs four layers of analysis on every piece of structured data found on your page:

Format Detection and Extraction. The tool finds all structured data on the page regardless of format. It extracts JSON-LD blocks from script tags, detects Microdata through itemscope and itemprop attributes, and identifies RDFa markup through vocab and typeof declarations. Many pages use multiple formats simultaneously — often unintentionally — and the audit surfaces all of them so you can identify conflicts or redundancies.

Schema Validation. Each extracted schema is validated against Schema.org specifications and Google's rich result requirements. The validator checks for missing required properties, missing recommended properties, incorrect property types, and type-specific rules (like headline length for Articles or step completeness for HowTo). Issues are categorized as errors (will prevent rich results), warnings (may reduce effectiveness), and informational notes.

AI Search Readiness. Every schema also receives an AI Search Readiness score from our AI Search Optimizer engine. This measures how well the structured data communicates entity clarity, content alignment, relationship depth, citation readiness, and freshness signals to AI-powered search engines like ChatGPT Search, Perplexity, and Google AI Overviews.

Completeness Scoring. For each schema type, we calculate what percentage of recommended fields are populated. An Article schema with only headline and datePublished might be technically valid but scores low on completeness because it's missing image, author, publisher, description, dateModified, and other properties that improve search performance.

Missing Opportunities: Schema Types You Should Have

The most valuable part of a schema audit is identifying what's missing, not just what's broken. Our tool analyzes the HTML content around the structured data to detect schema types that should be present but aren't. If the page has breadcrumb navigation but no BreadcrumbList schema, you're missing an easy rich result. If there are FAQ-style questions in the content but no FAQPage schema, you're leaving search visibility on the table. The audit detects patterns for BreadcrumbList, FAQPage, Article, Product, Recipe, Event, HowTo, LocalBusiness, Organization, and WebSite schema types.

Quick Wins: Maximum Impact, Minimum Effort

Not all improvements are equal. Adding a single missing field might unlock a rich result, while restructuring an entire schema might yield marginal gains. The Quick Wins section prioritizes improvements by effort level: "minimal" changes (adding a single property or attribute), "easy" changes (adding a new schema block for detected content), and "moderate" changes (expanding sparse schemas with recommended properties). This prioritization helps teams with limited resources focus on the changes that matter most.

How to Get Your Page Source

Since this tool runs entirely in your browser (no data is sent to any server), you need to provide the page source directly. The easiest way: right-click on your page, select "View Page Source" (or press Ctrl+U / Cmd+U), then select all (Ctrl+A / Cmd+A), copy (Ctrl+C / Cmd+C), and paste into the input field. This captures the full HTML including all structured data blocks, which is necessary for the missing opportunities analysis.

You can also paste standalone JSON-LD snippets if you just want to validate and score a specific piece of markup. However, the missing opportunities detection only works with full HTML source, since it needs to analyze the page content to suggest additional schema types.

Common Schema Audit Issues

Across thousands of audits, certain issues appear repeatedly. Missing dateModified is the most common — it's not required by Google's rich result specs, so it often gets overlooked, but it's critical for AI search freshness signals. Incomplete author objects are the second most common issue: many sites include just a name string instead of a Person object with @type, name, url, and credentials. Other frequent findings include missing @context declarations, image properties using relative URLs instead of absolute, and publisher objects without logos.

Duplicate or conflicting schemas are another common finding. Some CMS platforms inject schema markup automatically (often via plugins), and developers may also add their own JSON-LD blocks. The audit reveals all schemas on a page so you can identify duplicates — having two Organization schemas with different data, for example, can confuse search engines about which one is authoritative.

The Downloadable Report

The audit generates a clean, printable HTML report that includes all findings: schema types found, validation results, AI readiness scores, missing opportunities, and quick wins. This report is designed for sharing with development teams, clients, or stakeholders who need to understand the current state of a page's structured data and what improvements to prioritize. Download it directly from the tool interface.

Best Practices for Schema Maintenance

Schedule schema audits quarterly, or whenever you make significant content changes. Treat structured data like any other part of your codebase: include it in code reviews, validate it in CI/CD pipelines, and monitor for regressions. Use our AI Search Optimizer when creating new schemas to ensure they meet AI readiness standards from the start, rather than retrofitting later.

Related Tools

Optimize individual schemas for AI search with our AI Search Optimizer. Create new JSON-LD markup with the JSON-LD Generator. Validate specific schemas with the Schema Validator. Browse all available types in our Schema Type Reference.