ToolPortal
Technical SEO Tool

Schema Checker for JSON-LD

Paste JSON-LD, run a live check, and get structured feedback on missing required and recommended fields by schema type. This page is built for publish-time QA, not generic documentation.

Syntax validationType detectionRequired vs recommended gap listCopy-ready remediation checklist

Input

Paste a single entity, an array of entities, or an @graph payload.

Output

Results update from your exact payload, including per-entity field gaps.

Syntax: waiting for input
Entities: 0
0Missing required
0Missing recommended
0Warnings

Missing Required Fields

    No required-field issues yet.

    Missing Recommended Fields

      No recommended-field issues yet.

      Warnings

        No warnings yet.

        Remediation Checklist

          Run a check to generate actionable remediation items.

          {"status":"waiting_for_input","tip":"Paste JSON-LD and click Run Schema Check"}

          What Is Schema Checker?

          A schema checker is a practical QA step between content editing and production publishing. It validates whether your JSON-LD payload is syntactically valid JSON, identifies the entity types you declared, and verifies whether each type contains the fields search engines typically expect. This matters because schema errors are often silent: a page can still render correctly while structured data quietly fails eligibility checks for rich results.

          ToolPortal’s checker is designed around daily publishing workflows. Instead of returning only pass or fail output, it splits findings into missing required fields, missing recommended fields, and implementation warnings. Required issues represent direct blockers; recommended gaps affect quality and consistency; warnings call out format or structure problems that usually create downstream debugging friction. The result is intentionally operational: you can hand it to a writer, SEO manager, or developer and they can act on the same checklist immediately.

          You can validate a single schema object, an array of objects, or an @graph payload with multiple entities. The checker reports by entity index so teams can isolate fixes quickly. After all required fields pass, run one final rich-result validation in your external QA stack before publish. This page is built as a fast preflight layer, not a replacement for final search-engine verification.

          How to Validate JSON-LD

          1. Paste full payload: Include the exact JSON-LD planned for production, not a shortened draft. If you use @graph, keep all connected entities in one check.
          2. Run schema check: Confirm syntax first. If syntax fails, fix parsing before reviewing field-level findings.
          3. Resolve required gaps: Required fields are the first priority because missing items here are the most common reason markup gets ignored.
          4. Address recommended gaps: Recommended fields are often optional in strict terms but critical for richer result quality and consistency.
          5. Review warning logic: Warnings identify practical issues such as weak nested objects, missing context hints, or incomplete arrays.
          6. Copy remediation checklist: Use the generated checklist in your ticket, PR description, or editorial handoff notes.

          Worked Examples

          Article Markup Preflight

          A content team loads an Article payload before release. The checker catches a missing author.name and missing image, so the team patches metadata before the page goes live.

          Product Launch QA

          An ecommerce editor validates Product markup and sees that offers.priceCurrency is absent. The checker flags it before Search Console reports a delayed warning.

          Local SEO Rollout

          A multi-location site pastes LocalBusiness entities in one @graph. The output highlights missing address blocks on two entities, avoiding incomplete rollout across locations.

          Frequently Asked Questions

          Does this checker validate by schema type?

          Yes. It maps each detected type to a required and recommended field set, then reports missing items per entity so fixes are traceable.

          Can I validate multiple entities in one payload?

          Yes. Arrays and @graph structures are supported. The result labels entity numbers so you can patch each object without guesswork.

          What is the difference between required and recommended?

          Required fields are treated as blockers in this checker. Recommended fields are quality enhancements that often improve consistency and rich-result readiness.

          Should I still use external rich-result validators?

          Yes. Use this page as a fast preflight gate, then run external validation as the final deployment check in your SEO workflow.

          How do I share fixes with non-technical teammates?

          Use the copy checklist action. It exports a clean, line-by-line remediation list suitable for tickets, editorial tasks, and QA handoffs.