A first run with a JSON-LD structured data checker is a three-stage local workflow: paste the HTML you actually delivered, review the extracted JSON-LD and Microdata items separately, then validate the deployed URL with the official search-feature tool. Getting started is not a single click on "validate" — it is a sequence that begins before any extraction happens, because the output of the local checker is only as trustworthy as the source you paste. The Structured Data Checker & Extractor is built around that order: it parses pasted HTML inside a detached browser fragment, never fetches a URL, never executes scripts from your source, and never sends the document anywhere. What you get back is an inventory of what your markup contains, broken into parse errors, declared items, and focused property warnings for the supported profiles. That separation is the point of getting started, because it lets you answer the concrete question, "what does my template actually emit," before you start asking the wider question, "will Google show a rich result." The two questions need different tools, and confusing them is the most common first-run mistake.
Getting Started Means a Local Paste, Not a URL Submission
The word "started" is the part of the search that matters. A first-run workflow with a structured data checker does not start with a search-engine URL validator, and it does not start with a deployed page. It starts with the HTML your template actually emits and a paste action into a local browser tool. The Structured Data Checker & Extractor is designed around that fact: there is no URL field that triggers a fetch, no server upload, and no script from your source ever executes. The interface parses a detached template fragment inside your browser and renders the extracted values as text, which keeps the first run fast, deterministic, and private.
This matters because the most common first-run confusion is treating a local extraction as if it were a search-engine verdict. It is not. The tool reports facts about what is in the pasted markup. Eligibility — whether a search product actually shows a rich result — depends on visible content, feature policy, deployment, indexing, and decisions made by the search engine itself. Getting started means accepting that two separate questions need two separate tools, in a specific order, and that the boundary between them is deliberately enforced.
What to Prepare Before Your First Paste
Before you touch the tool, decide which version of the page you intend to paste. Three realistic sources exist, and they often disagree:
- The original HTTP response — what your server sent before any client-side framework touched it.
- The rendered DOM export — what the browser produced after JavaScript ran.
- The crawler-visible output — what a search-engine bot actually receives, which can differ from both of the above.
For a server-rendered template, the original response is usually the right input. For a single-page app or a framework that injects structured data after load, a controlled rendered-DOM export is the only honest input — paste whatever your test environment produces, not whatever your build pipeline produces. If a framework injects markup after load, compare all three views before drawing conclusions from a single extraction.
The paste is bounded by input size and item count so an accidental full-site dump cannot freeze the page, but you should still paste a representative template fragment rather than an entire site. The point of getting started is one specific question — does this template emit what I think it emits — not a global audit.
Run Your First Extraction in the Structured Data Checker & Extractor
- Open the Structured Data Checker & Extractor in your browser. Do not enter a URL — paste is the input.
- Copy the delivered HTML source, or a controlled rendered-DOM export, and paste it into the input area.
- Run the check. Parsing happens locally in a detached template fragment; nothing is fetched, no script from your source runs, and no document is sent to a remote server.
- Review parse errors separately. A malformed script block is reported on its own so one broken JSON-LD object does not erase valid items found elsewhere in the same document.
- Walk the JSON-LD item inventory. Single objects, top-level arrays, and @graph nodes are expanded into individual items while preserving their declared @type values and visible properties.
- Walk the Microdata inventory alongside. The tool reads itemscope, itemtype, and itemprop from ordinary HTML attributes and builds a bounded property view for each top-level item, with nested items remaining visibly nested rather than flattened into unrelated strings.
- Read the focused missing-property warnings last. Each warning is tied to an explicit, inspectable profile for a supported type — an unknown type is still extracted but is not assigned invented requirements.
That order — input, parse errors, item inventory, focused warnings — is the order the tool surfaces information, and it is the order you should read it. Skipping ahead to the warnings is a common first-run habit that hides far more useful triage signals.
Reading the Output on Your First Pass
First-pass reading is mostly triage. The output is intentionally split into sections so you can decide what to fix first. The table below summarizes what each section reveals and what it deliberately leaves unsaid.
| Output section | What it tells you | What it does not tell you |
|---|---|---|
| Parse errors | Whether a script block is valid JSON and where the failure sits. | Whether valid JSON describes accurate, visible content. |
| JSON-LD item inventory | Whether the intended @type is present, how many items a template emits, whether containers (arrays, @graph) expanded correctly, and whether a template emitted duplicate items. | Whether values match the visible page, follow search policy, or are eligible for a specific rich result. |
| Microdata item inventory | Whether itemscope, itemtype, and itemprop attributes are present, whether nested items stay nested, and which attribute actually exposed each value (content attributes, links, media source attributes, date values, or text content). | Whether Microdata is the right format for your target search feature, or whether the visible content supports the markup. |
| Focused property warnings | Whether a property listed in an explicit, documented profile is missing from a specific item. | Whether a search engine will reject the page, or whether an unlisted property still matters to another consumer. |
A warning means the local profile did not find a property on the item you pasted; it does not prove rejection. Valid JSON can describe inaccurate, hidden, or irrelevant content, and a complete-looking object can still violate policy or fail a deployment test. Conversely, Schema.org properties that are not part of a Google feature can still be meaningful to other consumers. Treat each warning as a prompt to consult the linked documentation, not as a verdict. For a deeper look at what each item in the inventory actually reveals about your template, the extraction-focused walkthrough covers the same input order from a slightly different angle.
Format Boundaries That Matter on a First Run
Before assuming the inventory is complete, confirm which formats the tool actually inspects. The Structured Data Checker & Extractor extracts JSON-LD and Microdata only. RDFa is outside the current product boundary and is never counted as absent or invalid structured data by this checker — it is simply not part of the extraction surface. That limitation is shown so the result cannot be mistaken for a universal semantic-web validator.
This has two practical consequences. First, a page that relies on RDFa will produce an empty or partial inventory even when the markup is valid for its target consumer. Second, pages that mix JSON-LD and Microdata in the same template will show both inventories side by side, but no attempt is made to reconcile duplicate types between them. If your template mixes formats, the local checker tells you what each format contributed, and you decide whether that was intentional.
Equally important: no attribute or value from the pasted document is interpreted as live behavior. The tool does not follow links, load images, submit forms, or evaluate embedded JavaScript. Input size and item counts are bounded so a runaway paste cannot freeze the page. These controls are what keep extraction deterministic, and they are also what make the inventory a true reflection of the pasted source rather than a side effect of evaluating it.
What to Do After Your First Extraction
The first extraction answers questions about your template. The next step answers questions about deployment. Move on to the deployed URL and validate it with the official tool for the search feature you actually target — for Google features, that means the Rich Results Test on the public URL, with the Google Search Central introduction to structured data as the documentation anchor for which formats and types each feature accepts.
Two reasons this is non-negotiable. First, the local checker parsed pasted HTML; the public URL may serve something different because of caching, CDN transforms, or conditional logic that varies by user-agent. Second, search-feature eligibility depends on consumer-specific rules that the local checker deliberately does not invent. Fix the source template, redeploy, then validate the deployed response. Repeat until both stages — local extraction and public validation — agree.
For long-running monitoring, layer in Search Console enhancement reports after recrawling, and test the actual page template across representative records rather than a single example. Search engines decide whether and when enhanced presentation appears, so no local checker can guarantee indexing, ranking, or a rich result. Treat the local tool as a transparent preflight, not a promise.
First-Run Questions Worth Answering Before You Iterate
Three questions tend to come up during a first run, and each has a specific answer that prevents later rework.
Does a clean result guarantee a Google rich result? No. The checker reports facts about pasted markup; eligibility also depends on visible content, feature-specific policy, deployment, indexing, and decisions the search engine itself makes. A clean extraction is a necessary but not sufficient condition.
Does the checker fetch or run my webpage? No. It parses pasted HTML inside a detached browser template fragment and does not request URLs, execute scripts, load resources, or submit the source anywhere. The boundary is deliberate — it avoids cross-origin failures, private-network requests, and misleading audits of source that may differ from what a crawler receives.
Why is an unfamiliar Schema.org type extracted but not graded? Extraction can be general because Schema.org is a broad vocabulary, but required-property rules are consumer and feature specific. The tool avoids inventing a validation profile where no documented contract exists. An ungraded type is not a failure — it is the tool staying inside its stated boundary, and a sign that you should consult the targeted search product's own documentation for that type.
One last habit worth forming on the first run: do not add fields merely to silence a warning. Required-property guidance is not a substitute for content review. Markup should represent content users can actually see and should use specific, accurate values. Fewer complete and truthful properties are preferable to a large object filled with generic or fabricated data — the latter is actively less trustworthy, both to consumers and to the search products that grade it.
If you're weighing options, Schema Markup Generator for Large Text: Safe JSON-LD covers this in detail.