Yes, you can run a JSON-LD and Microdata checker without uploading any file, because the Structured Data Checker & Extractor parses pasted HTML inside a detached browser template fragment rather than sending the source to a remote server. The boundary is deliberate: nothing about the document you paste is requested, fetched, executed, or transmitted. You copy the HTML that your CMS, build pipeline, or staging environment delivered, drop it into the input area, and the tool reads it entirely in your browser tab. This is the direct answer to the question of whether structured data review has to mean handing your template source to a third party, and it does not, and for any page that includes private URLs, internal product SKUs, draft copy, or restricted analytics identifiers, that distinction is the entire point. A no-upload checker also removes cross-origin fetches that fail behind VPNs, on intranet hosts, or against pre-production domains, and it removes the temptation to treat whatever an auditor saw as identical to what a crawler actually receives.
What "without uploading" actually means in a structured data checker
When a structured data tool claims to work without uploading a file, three concrete promises should hold at the same time. First, the page URL is never requested, so cross-origin failures, intranet access, and bot-protected staging environments cannot break the review. Second, the pasted HTML is parsed inside a detached document fragment, so no <script> from your source is evaluated, no image or font is fetched, and no link is followed. Third, the bytes never leave the browser tab, so the source is not logged, cached server-side, or exposed to a third-party analytics pipeline.
The Structured Data Checker & Extractor meets all three. It uses a detached browser template fragment that keeps parsed nodes outside the live page and renders bounded extracted values as text, so the interface never evaluates embedded JavaScript, submits forms, or loads media. Input size and item counts are intentionally bounded so an accidental full-site dump cannot freeze the page, and these limits are surfaced rather than hidden so an editor who exceeds them gets a clear signal rather than a silent failure.
How to check JSON-LD and Microdata locally in your browser
The workflow is built around three steps that map to the tool's operating contract.
- Paste the delivered HTML source or a controlled rendered-DOM export into the input area. If your framework injects markup after load (client-side rendering, hydration, or a tag manager), compare the original response, the rendered DOM, and the crawler-visible output before drawing conclusions, because what you paste is what gets checked.
- Run extraction, then review the report in separate sections: JSON-LD parse errors, Microdata items, declared types and properties, and focused missing-property warnings for the supported profiles. A malformed JSON block is reported on its own so one broken script does not erase valid items found elsewhere in the same document.
- Fix the source template based on what the inventory shows, redeploy, and then validate the public URL with the official validator for the search feature you actually target, for example Google's Rich Results Test when the type targets a Google feature.
The order matters. Parse errors block interpretation, so they come first. Profile warnings come next, because they compare the extracted properties against an explicit documented rule set rather than against the entire Schema.org vocabulary. Policy questions about visible content, accuracy, and feature-specific eligibility are not the local checker's job; they are the reason an official validator on a deployed URL exists.
What the extraction report shows you
JSON-LD comes in three shapes the checker handles individually: a single object with one @type, an array of objects each with its own @type, and an @graph container holding several nodes that share a document context. The checker expands each container into individual items, preserves the declared @type values, and lists the visible properties of each so an editor can answer a concrete question such as "did a template emit duplicate items?" or "is a required field missing from one variant?".
For Microdata, the parser walks the document and builds a bounded property view for top-level items. It recognises values exposed through content attributes, links, media source attributes, date values, and text content. Nested items remain visibly nested rather than being silently flattened into unrelated strings, which is the failure mode that turns Microdata audits into guessing games. A blank URL field, for example, often signals that the wrong attribute was used on the anchor, and the nested-property view makes that visible without further inspection.
The report separates three categories of finding. Parse errors mean the JSON did not decode or the Microdata attributes were structurally invalid. Missing focused properties come from explicit, documented profiles only; an unknown type is still extracted but is not assigned invented requirements. Informational observations flag limitations such as RDFa being outside the supported boundary, so the result cannot be mistaken for a universal semantic-web validator.
Why local-only parsing changes the review process
A no-upload checker removes the most common reasons a markup audit produces a misleading verdict. The audited page may have been redirected, gated, or stripped of structured data for non-browser user agents, in which case a server-side fetcher sees something different from what the editor authored. The third-party auditor may have logged or cached sensitive source code, which is a compliance issue for internal templates and draft records. By pasting what your system actually delivered, you align the audit with the source of truth you control.
Google's structured data documentation recommends JSON-LD for many features because it is generally easier to maintain, and the WHATWG HTML Microdata specification defines the itemscope, itemtype, and itemprop model that the checker parses. Both references are read locally rather than fetched; the tool does not call them, it simply applies the same parsing model locally to the bytes you paste.
| Aspect | JSON-LD | Microdata |
|---|---|---|
| Where it lives | A <script type="application/ld+json"> block, separate from visible HTML | Inline attributes (itemscope, itemtype, itemprop) on ordinary HTML elements |
| Format | JSON object, array, or @graph | HTML with attribute annotations |
| Recommended by Google | Yes, for many features | Supported where documented |
| Ease of template maintenance | Easier, isolated from layout | Harder, markup is interleaved with content |
| Spec or reference | Google structured data intro | WHATWG HTML Microdata |
| Validator behaviour | Parsed as JSON; malformed blocks reported separately | Parsed by walking the document; nested items stay nested |
Limits the tool calls out explicitly
Three boundaries are stated up front and worth understanding before you trust the report. Required-property guidance is not a substitute for content review, so markup should describe content users can actually see, with specific, accurate values, because adding fields merely to silence a warning can make the implementation less trustworthy. Validation is intentionally narrower than extraction, so Schema.org defines a broad vocabulary, but search products publish their own required and recommended properties for specific experiences, and the checker applies explicit profiles only where it has a documented rule set. RDFa is outside the current product boundary and is never counted as absent or invalid structured data; if your page uses RDFa, the checker will not see it, and that limitation is shown so the result is not mistaken for a universal semantic-web validator.
After the local check: official validation steps
A clean local extraction means the markup parsed and the focused profiles did not flag missing properties. It does not mean the page is eligible for a Google rich result, and no local checker can guarantee indexing, ranking, or enhanced presentation. To close the loop, run Google's Rich Results Test on the deployed URL when the type targets a Google feature, inspect Search Console enhancement reports after recrawl to see how Google interpreted the deployed response, and test the actual page template across representative records, because a template can emit valid JSON-LD for one variant and a broken block for another. Resolve parse errors first, then profile warnings, then policy questions about visible content. The local checker is a transparent preflight rather than a final verdict, and its output is evidence about the pasted document rather than a promise about search appearance.