Structured Data Validator
Validate JSON-LD schema markup and fix errors before publishing
Introduction
A structured data validator is a tool that checks whether your JSON-LD schema markup is correctly formatted and follows schema.org standards. JSON-LD (JavaScript Object Notation for Linked Data) is the code you add to your web pages that tells search engines like Google exactly what your content represents—whether it's an article, product, FAQ, recipe, event, or any of hundreds of other content types. The validator parses this code, checks for syntax errors, verifies that required properties are present, and warns you about missing recommended fields that could improve your rich result eligibility. This matters tremendously for SEO because structured data is what enables rich results in Google search—those enhanced listings with star ratings, product prices, recipe images, FAQ accordions, event dates, and other visual elements that make search results more attractive and informative. Without properly validated structured data, your content cannot qualify for these enhanced displays. The challenge is that small mistakes can silently break everything. A missing comma, a forgotten @context property, or using a string where an object is expected will cause Google to reject your entire schema block. Our validator catches these issues before deployment by checking JSON syntax errors, validating schema structure, verifying required properties for common types, and providing clear error messages with fix suggestions.
Who Should Use This Tool?
- SEO professionals validating schema markup before publishing content
- Web developers implementing structured data in websites and applications
- Content creators ensuring articles and blog posts qualify for rich results
- E-commerce managers optimizing product schema for enhanced search listings
- Technical writers documenting structured data implementations
- Agency teams performing schema audits for client websites
- Digital marketers troubleshooting rich result eligibility issues
- WordPress plugin developers building schema generation tools
- Local business owners implementing LocalBusiness schema
- Recipe bloggers validating Recipe schema for cooking instructions
How This Tool Works
Our structured data validator works entirely in your browser to provide instant feedback on your JSON-LD markup without sending your data to any server. When you paste your JSON-LD code into the validator, it first attempts to parse the JSON using strict parsing rules—if there are syntax errors like missing commas, unmatched brackets, or unescaped quotes, you'll see exactly where the error occurred with line and column numbers. Once the JSON parses successfully, the validator examines the structure looking for required properties like @context (which should be https://schema.org) and @type (which identifies what kind of content this represents). The tool recognizes common schema types including Article, BlogPosting, NewsArticle, FAQPage, Product, Organization, LocalBusiness, BreadcrumbList, and Person, applying type-specific validation rules for each. For example, Article schema requires headline, image array, author object, and datePublished, while FAQPage needs a mainEntity array where each element is a Question object with name and acceptedAnswer properties. The validator checks not just that properties exist, but that they have the correct data types—strings vs numbers vs objects vs arrays—and that nested structures follow schema.org specifications. Results are categorized into errors (blocking issues that prevent functionality), warnings (recommended improvements), and info (optional enhancements), with each issue showing the property path, description, and actionable fix suggestions. All validation happens client-side in JavaScript with zero server communication, ensuring complete privacy for testing production schema that might contain sensitive business information.
Try Structured Data Validator Now
Use the interactive tool below to get instant results
Paste the complete JSON-LD structured data from your webpage
Paste your JSON-LD structured data and click "Validate" to check for errors
What is a Structured Data Validator?
A structured data validator is a tool that checks whether your JSON-LD schema markup is correctly formatted and follows schema.org standards. JSON-LD (JavaScript Object Notation for Linked Data) is the code you add to your web pages that tells search engines like Google exactly what your content represents - whether it's an article, product, FAQ, recipe, event, or any of hundreds of other content types. The validator parses this code, checks for syntax errors, verifies that required properties are present, and warns you about missing recommended fields that could improve your rich result eligibility.
This matters tremendously for SEO because structured data is what enables rich results in Google search - those enhanced listings with star ratings, product prices, recipe images, FAQ accordions, event dates, and other visual elements that make search results more attractive and informative. Without properly validated structured data, your content cannot qualify for these enhanced displays, no matter how high quality your content is. Google's systems parse the JSON-LD on your pages to understand what you're offering, and if there are errors or missing required fields, your markup gets ignored completely.
The challenge with structured data is that small mistakes can silently break everything. A missing comma, a forgotten @context property, or using a string where an object is expected will cause Google to reject your entire schema block. Unlike HTML where browsers try to fix your mistakes, JSON-LD parsers are strict - it either works perfectly or fails completely. Many developers add structured data to their site, see no immediate errors, assume it's working, and only discover weeks later in Google Search Console that their markup was invalid the whole time.
Our validator catches these issues before deployment. You paste your JSON-LD code, it checks for JSON syntax errors, validates the schema structure, verifies required properties for common types like Article, FAQPage, Product, Organization, and BreadcrumbList, and provides clear error messages with fix suggestions. This immediate feedback lets you test schema markup during development, fix issues before going live, and debug problems reported by Search Console without guessing what's wrong. The tool understands the specific requirements for different schema types - for example, knowing that Article requires headline, image, author, and datePublished, while FAQPage needs mainEntity with Question objects that each contain acceptedAnswer with Answer objects. Getting these nested structures right is tricky, and the validator guides you through it.
How to Use the Structured Data Validator
Copy Your JSON-LD Code
Open your webpage's source code and locate the JSON-LD script block (usually found in the head or body section wrapped in script type="application/ld+json" tags). Copy everything between the opening and closing script tags - just the JSON code itself, not the script tags. You can also load one of our example schemas to see how validation works with different content types.
Paste Into the Validator
Paste your JSON-LD code into the large text area on the left side. The validator accepts any schema.org type, but has specific validation rules for Article, BlogPosting, NewsArticle, FAQPage, Product, Organization, LocalBusiness, BreadcrumbList, and Person. If you want to see fix suggestions along with errors, enable the "Show Fix Suggestions" checkbox before validating.
Run Validation
Click the "Validate Structured Data" button to check your code. The tool parses your JSON, checks for syntax errors, validates the schema structure, and verifies type-specific requirements. Processing happens instantly in your browser - there's no server delay, and your data never leaves your device. The validation typically completes in under a second even for complex nested schemas.
Review Results and Fix Issues
The validator shows a clear status (valid or failed), detected schema type, and a categorized list of issues. Errors must be fixed for your structured data to work - they indicate missing required properties or invalid JSON syntax. Warnings suggest improvements that could enhance rich result eligibility but won't break functionality. Each issue includes the property path, a description, and actionable suggestions for fixing it. Address all errors first, then tackle warnings to optimize your markup.
Key Features
Comprehensive Syntax Validation
Detects JSON parsing errors with precise line and column numbers, making it easy to locate and fix syntax issues like missing commas, unmatched brackets, or unescaped quotes. Proper JSON syntax is the foundation - if the JSON doesn't parse, nothing else matters.
Automatic Schema Type Detection
Recognizes common schema.org types from the @type property and applies type-specific validation rules. Knows that Article requires different properties than Product, which needs different fields than FAQPage. Validates nested types correctly for complex schema structures.
Clear Error Categorization
Separates issues into errors (blocking problems that prevent functionality), warnings (recommended improvements), and info (optional enhancements). Color-coded indicators make it obvious what's critical versus what's optional, helping you prioritize fixes effectively.
Precise Error Location Reporting
Shows exactly which property path has the issue, like "mainEntity[2].acceptedAnswer.text" for nested array elements. You don't have to hunt through complex JSON structures - the validator pinpoints the exact location of every problem with property path notation.
Actionable Fix Suggestions
Provides specific recommendations for resolving each issue, not just identifying what's wrong. Suggests the exact property to add, the correct data type to use, or the proper nesting structure. Makes fixing issues straightforward even if you're not a schema.org expert.
Complete Client-Side Privacy
All validation executes in your browser with zero server communication. Your structured data never leaves your device, ensuring complete privacy for testing production schema that might contain sensitive business information. Works offline after the page loads.
When to Use a Structured Data Validator
Validating FAQ Schema Before Publishing Content
FAQ-rich snippets are among the most valuable rich results because they take up enormous space in search results and directly answer user questions before they even click. When you're creating content with questions and answers - whether it's a dedicated FAQ page, a blog post with an FAQ section, or product pages with common questions - validating your FAQPage schema before publishing ensures you qualify for these prominent rich results. The structure is more complex than it appears - each question must be a Question object with a "name" property, and each answer must be an "acceptedAnswer" object containing an Answer object with "text" property.
Many content creators get the nesting wrong, using strings where objects are required, or forgetting the @type declarations for Question and Answer elements. The validator catches these structural errors immediately, showing you exactly which question in your mainEntity array is missing properties or has incorrect types. This is especially important because FAQPage schema is unforgiving - if even one question is malformed, Google may ignore your entire FAQ schema, not just the broken question.
By validating before publishing, you ensure your FAQ content launches with working schema that qualifies for rich results from day one. No waiting weeks to discover your FAQ markup was silently broken, no scrambling to fix issues after the content is live. Content teams can integrate validation into their editorial workflow - writers draft content with FAQ sections, developers add the JSON-LD schema, editors validate it before approval, and published content goes live with confidence that the structured data will work. This systematic approach dramatically increases the percentage of your content that actually achieves rich results in search.
Fixing Rich Result Eligibility Issues
When Google Search Console reports that your pages are "Not eligible for rich results" or shows structured data errors, figuring out what's wrong can be frustrating because Search Console's error messages are sometimes vague or delayed by days. You've added structured data to your pages, but Search Console says it's invalid or incomplete. A validator gives you immediate, detailed feedback about exactly what's wrong, without waiting for Google to recrawl your pages and report issues through Search Console.
Common rich result eligibility problems include missing required properties (like Article pages without "publisher" or Product pages without "offers"), incorrect data types (putting strings where numbers are expected), and invalid nested objects (like author property being a plain string instead of a Person object). The validator tests your actual JSON-LD against schema.org requirements, showing precisely which properties are missing or malformed and providing suggestions for fixing them.
This diagnostic workflow is essential for SEO professionals troubleshooting rich result issues. You pull the JSON-LD from a page that's not showing rich results, paste it into the validator, identify the specific errors, fix them in your CMS or template, revalidate to confirm the fix works, and then request reindexing in Search Console. What could be a week-long investigation with multiple rounds of changes becomes a same-day fix when you can instantly test your schema modifications. The validator helps you move from guessing what Google wants to knowing exactly what's broken and how to fix it.
Auditing Schema for Large Content Sites
Large blogs, news sites, and content publishers with thousands of pages need to ensure structured data is implemented consistently across all content types - blog posts with Article schema, category pages with CollectionPage schema, author pages with Person or ProfilePage schema, and so on. When structured data is generated programmatically from CMS templates, a single error in the template propagates to every page, potentially breaking schema for hundreds or thousands of URLs. A structured data validator becomes essential for quality assurance during site-wide implementations or template changes.
SEO teams use validators to spot-check sample pages from each template after deploying new structured data code. They test pages from different categories, authors, dates, and content types to ensure the dynamic schema generation works correctly in all scenarios. For example, they might discover that author pages work fine for authors with profile pictures but fail for authors without images because the template assumes image URLs are always present. Or they might find that old articles published before a certain date are missing schema properties that newer articles have, revealing a migration gap.
This systematic validation approach catches edge cases before they become widespread problems. Rather than waiting for Search Console to discover and report errors across thousands of pages (which can take weeks), content platforms validate representative samples, identify template issues early, fix them at the template level, and ensure all future pages will have correct schema. For large publishers, this can mean the difference between 80% of pages qualifying for rich results versus only 20% because a template bug silently broke schema site-wide. Regular validation audits become part of the content operations workflow, catching regressions after platform updates or plugin changes.
E-commerce Product Schema Testing
E-commerce sites depend heavily on Product schema for rich results that show prices, availability, ratings, and images directly in search results. These enhanced listings significantly impact click-through rates and conversions - users can see product pricing and ratings before clicking, which pre-qualifies traffic and increases purchase intent. But Product schema has specific requirements: name and image are always required, offers should include price, priceCurrency, and availability, and aggregateRating needs ratingValue and reviewCount. Missing any of these can disqualify products from rich results, costing you valuable visibility.
E-commerce platforms often generate Product schema dynamically from product databases, and errors creep in with missing fields (like products without images, out-of-stock items without availability status, or sale prices without regular prices for comparison). A validator helps QA teams test products in different states - in stock, out of stock, on sale, with reviews, without reviews - to ensure the schema adapts correctly to all scenarios. They catch issues like offers objects missing @type property, prices formatted as strings with currency symbols instead of plain numbers with separate priceCurrency, or aggregateRating objects with totalReviews instead of the required reviewCount property.
For large catalogs with thousands of products, validating a representative sample from each category, price range, and inventory status reveals template issues before they affect the entire catalog. You might discover that products without manufacturer images fall back to placeholder images that aren't properly encoded in the schema, or that variant products (different sizes or colors) generate duplicate schema that confuses Google. By testing and fixing these patterns systematically, e-commerce sites maximize the percentage of products eligible for rich results, directly impacting organic search revenue. Validation becomes part of the product publishing workflow, with QA checking schema before products go live.
Agency SEO Audits and Client Onboarding
SEO agencies and consultants use structured data validators as standard audit tools when onboarding new clients or performing technical SEO assessments. Checking whether a site has structured data and whether it's implemented correctly is a fundamental part of any SEO audit, and validators make this process systematic and documentable. Agencies pull the JSON-LD from key client pages, validate each schema type, document errors and missing properties, and provide prioritized recommendations for fixes that will improve rich result eligibility.
Common findings during client audits include completely missing structured data (surprisingly common even in 2024), outdated Microdata format that should be converted to JSON-LD, schema with multiple blocking errors that prevent any rich results, or schema that's technically valid but missing recommended properties that would enhance display quality. The validator helps agencies quantify the opportunity - showing clients concrete evidence that their blog posts are missing Article schema, their product pages have invalid Product markup, or their FAQ pages are structured incorrectly, all of which prevents them from competing for rich results that competitors are capturing.
Agencies integrate validation into their ongoing client management too. After implementing schema fixes or launching new content types with structured data, they validate sample pages to confirm everything works before reporting success to clients. When clients report drops in rich result visibility or Search Console shows new structured data errors, agencies use validators as first-line diagnostic tools to quickly identify whether the issue is schema-related or something else. This systematic approach to structured data quality control becomes part of the agency's value proposition - clients know their schema is being monitored and maintained correctly, not just implemented once and forgotten.
Local Business Schema Validation
Local businesses - restaurants, service providers, retail stores - use LocalBusiness schema to provide Google with structured information about their locations, hours, contact details, and services. This schema powers local search features, Google Maps integration, and local pack rankings. Getting LocalBusiness schema right is critical because it directly impacts whether your business shows up prominently in local searches, displays correct information in Google's business cards, and appears with accurate hours and contact details.
LocalBusiness schema has specific requirements: name and address are mandatory, address must be a properly structured PostalAddress object with @type and relevant country-specific properties, telephone or contactPoint should be included, and properties like openingHours, priceRange, and geo coordinates are recommended for better local search performance. Many local businesses implement LocalBusiness schema incorrectly - using plain text for addresses instead of structured PostalAddress objects, providing relative URLs instead of absolute ones, or missing critical properties like addressCountry that help Google understand location context.
A validator helps local business owners and their web developers ensure their schema is complete and correctly structured before publishing. They test their LocalBusiness markup, fix any missing required properties, add recommended fields like geo coordinates for more precise map placement, and verify that multi-location businesses have consistent schema across all location pages. This is particularly important for businesses trying to improve local SEO - properly validated LocalBusiness schema is foundational to local search success, and errors here directly impact visibility in Google Maps and local pack results. Web agencies managing local business websites use validators to QA their LocalBusiness implementations before launch and to audit existing client sites for improvement opportunities.
Debugging Google Search Console Structured Data Errors
Google Search Console's Enhancement reports show structured data errors and warnings for your site, but the error messages are sometimes cryptic or don't provide enough detail to understand exactly what's wrong. You might see errors like "Missing field 'image'" or "Invalid property type" affecting dozens or hundreds of pages, but Search Console doesn't always show you the actual JSON-LD code or explain why Google thinks there's a problem. A validator bridges this gap by letting you test the exact structured data from affected pages and get detailed explanations of what's wrong.
The workflow is straightforward: Search Console reports structured data errors on certain pages, you view the source of one of those pages and copy the JSON-LD code, paste it into the validator, and immediately see detailed error descriptions with property paths and fix suggestions. This often reveals issues that weren't obvious from Search Console's generic error messages - like image properties containing relative URLs instead of absolute ones, nested objects missing required @type declarations, or properties using incorrect capitalization (Schema.org properties are case-sensitive).
This debugging process is much faster than the traditional approach of changing your schema, waiting for Google to recrawl, and checking Search Console days later to see if the errors cleared. With a validator, you test fixes immediately, iterate until the schema validates correctly, deploy the working version, and request reindexing with confidence that the errors will resolve. Technical SEOs handling multiple client sites use validators to quickly triage Search Console structured data errors without spending hours interpreting vague error messages or making multiple rounds of trial-and-error fixes. The validator becomes the debugging tool that translates Search Console errors into actionable fixes.
Pre-Deployment QA for Site Launches and Migrations
Site launches, redesigns, and platform migrations are high-risk events where structured data often breaks silently. A site might migrate from WordPress to a custom CMS, or redesign templates and forget to update the schema generation logic, or upgrade plugins that change how JSON-LD is output. These changes can inadvertently break structured data across the entire site, and if not caught before launch, you discover the problem only after traffic drops and Search Console reports errors weeks later. Pre-deployment QA with a structured data validator catches these issues while the new site is still in staging.
Development and QA teams test representative pages from the staging environment - homepage, blog posts, product pages, category pages, author pages - copying the JSON-LD from each template type and validating it. They compare the new site's schema against the current production site's schema to ensure nothing was lost in migration. Common migration issues include schema plugins not being configured correctly in the new platform, custom schema code not being ported to new templates, or dynamic fields in templates using different variable names that result in missing properties. Catching these before launch prevents post-migration SEO disasters.
For major e-commerce migrations or large publisher redesigns, structured data validation becomes part of the pre-launch checklist alongside other critical tests. Teams validate schema for products in different states (in stock, on sale, with/without reviews), blog posts old and new, category pages shallow and deep in the site hierarchy, and all other page types that have structured data. This systematic validation across page types and content states ensures the new site launches with fully functional schema that preserves existing rich result eligibility. No traffic loss from broken schema, no scrambling to fix issues post-launch, and no waiting weeks for Google to recrawl and restore rich results after emergency fixes.
Frequently Asked Questions
What is structured data and why does it matter for SEO?
Structured data is code you add to your web pages that helps search engines understand your content in a more precise way. Think of it as labels and categories that explicitly tell Google "this is a product with a price," "this is an article with an author," or "these are questions and answers." It matters for SEO because structured data enables rich results - those enhanced search listings with ratings, prices, images, FAQs, and other eye-catching features that appear above regular blue links. Sites with properly implemented structured data often see higher click-through rates because their search listings stand out visually and provide more information upfront. Google uses structured data to determine eligibility for special result types like recipe cards, product carousels, job postings, and event listings. Without valid structured data, your content cannot qualify for these enhanced search features, no matter how high quality it is.
Does valid structured data guarantee rich results in Google?
No, and this is a crucial point that many people misunderstand. Valid structured data is necessary but not sufficient for rich results. Google's documentation explicitly states that having valid markup makes you eligible for rich results, but Google decides whether to display them based on multiple factors including content quality, search query relevance, user device, and whether competitors also have structured data. You can have perfectly valid schema markup and still not see rich results if your content is thin, if the query does not typically show rich results, or if Google determines that standard listings serve users better in that context. Additionally, Google manually reviews certain rich result types and may not show them if your site violates quality guidelines. Valid structured data is the entry ticket, not a guarantee of enhanced listings. However, invalid structured data definitely prevents rich results, so validation is still critically important.
What is the difference between JSON-LD, Microdata, and RDFa?
These are three different formats for adding structured data to web pages, but Google strongly recommends JSON-LD. JSON-LD is a JavaScript notation that lives in a script tag, completely separate from your HTML content - you add a script block to your page head or body, and it does not touch your visible content. Microdata embeds structured data directly into HTML tags using attributes like itemscope, itemtype, and itemprop, which means it is mixed with your content markup. RDFa is similar to Microdata but uses different attributes (typeof, property) and comes from a different standard. The practical difference is that JSON-LD is much easier to implement and maintain because you can add it or modify it without touching your content HTML, it is less prone to breaking when you update page templates, and it is easier for tools to parse and validate. Google explicitly recommends JSON-LD in their documentation, and most modern CMSs and plugins output JSON-LD by default. Unless you have a specific reason to use Microdata or RDFa, stick with JSON-LD.
What are the most common structured data validation errors?
The most frequent errors are missing required properties, incorrect data types, and syntax mistakes. Missing @context or @type properties is extremely common - these are required for valid JSON-LD, but developers often forget them when hand-coding schema. Missing required properties like "name" for Product, "headline" for Article, or "acceptedAnswer" for FAQ questions will cause validation failure. Type mismatches happen when you provide a string where a number is expected, or forget to wrap things in arrays when the schema requires it. JSON syntax errors like missing commas, unmatched brackets, or unescaped quotes break parsing entirely. Many people also struggle with nested objects - for example, Article requires an author with @type Person or Organization, but they just put a string name instead of a proper object. Invalid URLs are another issue - using relative URLs instead of absolute ones, or putting placeholder values like "example.com" that do not actually work. The validator catches these issues immediately, whereas Google Search Console might show errors days or weeks later.
How does Google treat warnings versus errors in structured data?
Errors prevent your structured data from working at all - if there are errors, Google cannot parse the markup and you will not be eligible for rich results. Warnings mean your structured data is technically valid and parseable, but you are missing recommended properties that could improve your rich result display or eligibility. For example, an Article without a "publisher" property will show an error because publisher is required, but an Article without "dateModified" will show a warning because it is recommended but not required. Google will still use structured data with warnings, but your rich results might be less informative or might not appear for certain query types. Some warnings indicate properties that are technically optional but practically important - like including an image URL for better visual display. The general rule is fix all errors immediately because they completely break functionality, and address warnings when possible to improve your results quality. However, not all warnings need fixing if the recommended properties genuinely do not apply to your content.
Can I have multiple schema types on one page?
Yes, absolutely, and this is actually quite common and recommended in many situations. You can include multiple separate JSON-LD script blocks on a single page, each with different @type values. For example, a blog post might have Article schema for the post content, Organization schema for the publisher, BreadcrumbList schema for navigation, and FAQPage schema if it contains questions and answers. An e-commerce product page might combine Product schema with aggregateRating, Organization schema for the seller, and BreadcrumbList for the category path. Each script block should be complete and valid on its own. Google processes all the structured data it finds on a page and uses what is relevant. The key is that each schema block should be internally consistent - do not split a single Article across multiple script tags, but multiple complete schemas of different types are fine. This actually helps Google understand your content more comprehensively from multiple angles.
Is this structured data validator safe for sensitive content?
Yes, completely safe. This validator runs entirely client-side in your browser using JavaScript - no data is uploaded to any server, no API requests are made, and nothing is stored or logged anywhere. You can verify this by opening your browser developer tools and checking the Network tab while validating - you will see zero outbound requests containing your structured data. All parsing, validation, and error reporting happens locally on your device. This is particularly important for structured data because it often contains business information like prices, locations, contact details, and product descriptions that you might not want to send to third-party services. Many online validators send your data to their servers for processing, which creates privacy risks and means your markup is exposed to external parties. Our client-side approach ensures your structured data remains private and under your control. The tool will work even without an internet connection after the page loads.
What are the limitations of structured data validators?
Validators can check syntax and schema compliance, but they cannot guarantee real-world performance or rich result eligibility. This tool validates that your JSON-LD is parseable, follows schema.org conventions, and includes required properties - but it cannot test whether Google will actually display rich results, whether your content quality meets Google's standards, or whether the structured data accurately represents your page content. Validators also cannot detect lies or spam - if you mark up low-quality content with Article schema or add fake review ratings, the validator will say it is valid even though Google will ignore it or penalize you. Some edge cases and advanced schema features may not be fully covered by validation rules. The validator checks structure, not truthfulness or quality. Additionally, schema.org has hundreds of types and thousands of properties - most validators including this one implement rules for the most common types but may not catch issues with obscure schema types. Always use validators as a first check, then test in Google Search Console, and ultimately monitor your actual search appearance to confirm everything works as expected.