Modern JSON-LD implementation in 2025: Toward cleaner and maintainable SEO

Yet JSON-LD structured data is now essential for content visibility in Google’s rich results. The key is implementing it properly.

In this article, we’ll explore a modern, maintainable, and typed approach to JSON-LD, ideal for JavaScript and TypeScript environments, so you can integrate structured data in a clean, robust way for the long haul.

What is JSON-LD and why should you care?

JSON-LD (JavaScript Object Notation for Linked Data) is the format recommended by Google for integrating structured data into a web page. It helps search engines understand page content by providing semantic context: is it an article? A recipe? A product page?

Unlike older formats (Microdata, RDFa), JSON-LD is separated from the HTML and typically embedded in a <script type="application/ld+json"> tag. That offers several advantages:

• No interference with HTML structure
• Easier to read and dynamically generate
• More maintainable in JS/TS codebases

Common use cases:

• Article, NewsArticle, BlogPosting: for editorial content;
• Product, Offer, Review: for e-commerce;
• Event, Organization, BreadcrumbList, and more.

The goal isn’t just better SEO visibility, it’s also about preparing for a more semantic and interoperable web.

Common implementation pitfalls

Even in 2025, many JSON-LD implementations suffer from common issues that can limit indexing or eligibility for rich results.

• Multiple scattered <script> tags: You’ll often see one schema per script, placed randomly in the DOM. This complicates reading, validation, and can cause conflicts.
• Duplicated data: It’s still common to see the same schema repeated, sometimes with inconsistent values, which sends mixed signals to Google.
• Incomplete or invalid structures: A NewsArticle without a headline, or an Organization with an invalid url, are frequent issues that reduce schema usefulness.
• Syntax and typing issues: Basic JSON mistakes (missing commas, wrong keys like "context" instead of "@context") are common, especially in untyped setups.

schema-dts: Typing and autocompletion for reliable schemas

To make JSON-LD more robust and maintainable, Google developed schema-dts, a powerful TypeScript helper.
It’s a set of TypeScript types generated from Schema.org’s vocabulary. It offers:

• Strong typing for your JSON-LD objects
• IDE autocompletion (VS Code, WebStorm..)
• Early error detection for required fields and structure
• Valid and predictable output
  

Example :

import { NewsArticle } from "schema-dts";

const articleSchema: NewsArticle = {
  "@type": "NewsArticle",
  headline: "Implementing JSON-LD cleanly in 2025",
  datePublished: "2025-08-04T07:00:00.000Z",
  author: {
    "@type": "Person",
    name: "John Doe"
  }
};

With typing, if you omit required fields (headline, datePublished, etc.), TypeScript will warn you, avoiding silent SEO failures.

Centralizing Schemas with @graph

One modern best practice is to centralize all structured data into a single script, using the @graph property. This:

• Reduces DOM clutter
• Simplifies validation (everything in one object)
• Makes entity relationships clearer

Here’s a real-world example inside a <script type="application/ld+json">, fully centralized using @graph:

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "name": "Example Blog",
      "url": "https://example.com",
      "logo": {
        "@type": "ImageObject",
        "url": "https://example.com/logo.png"
      }
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://example.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Blog",
          "item": "https://example.com/blog"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "JSON-LD Article"
        }
      ]
    },
    {
      "@type": "NewsArticle",
      "headline": "Modern JSON-LD Implementation in 2025",
      "datePublished": "2025-08-04T07:00:00.000Z",
      "author": {
        "@type": "Person",
        "name": "John Doe"
      },
      "publisher": {
        "@type": "Organization",
        "name": "Example Blog",
        "logo": {
          "@type": "ImageObject",
          "url": "https://example.com/logo.png"
        }
      },
      "mainEntityOfPage": "https://example.com/blog/json-ld-2025"
    }
  ]
}

Best Practices for 2025

• Centralize your schemas: Use a single <script> with @graph. Avoid scattering multiple <script> tags across your DOM.
• Always validate: Use tools like Google’s Rich Results Test or Schema.org validator to ensure eligibility and correctness.
• Use typing + runtime validation: Leverage schema-dts or tools like zod to enforce both static and runtime validation of schema objects.
• Avoid duplication: Each entity (e.g., Organization) should appear only once on a page, with consistent values across all schema objects.
• Keep it readable and maintainable: Organize your schema logic in reusable files or utilities (e.g., lib/schema.ts), and document relationships clearly.
• Don’t overload your pages: There’s no need to implement every available schema type. Focus on high-impact ones like Article, Product, BreadcrumbList, FAQPage, etc.

Clean JSON-LD, Stronger SEO

JSON-LD isn’t just a technical checkbox for SEO. It’s a foundation for building a cleaner, more understandable, and future-proof web.Mettre en place des données structurées en JSON-LD, ce n'est pas juste cocher une case SEO. C'est structurer l'information pour un web plus compréhensible, maintenable et interopérable.

With a typed (schema-dts), centralized (@graph), and validated setup, you can shift from quick fixes to solid architecture. And as a bonus, your schemas will be easier to debug and scale over time.

Clean JSON-LD means clearer semantics, better indexing, and code that search engines, and developers, can trust.