# Add schema.org JSON-LD for Agents

> Mark up your entities with schema.org JSON-LD so an agent can extract structured, typed facts in one parse, not just ambiguous prose.

## What JSON-LD is

JSON-LD (JSON for Linking Data) embeds structured, machine-readable facts about your page inside a `<script type="application/ld+json">` block, using the shared schema.org vocabulary. Instead of leaving an agent to infer that a string is a price, a date, or an author, you state it explicitly as typed data — a `Product` with an `offers.price`, an `Article` with a `datePublished`, an `Organization` with a `sameAs` link to its other identities.

## Why it matters for agents

Prose is ambiguous; typed data is not. JSON-LD lets an agent lift exact facts in one parse without natural-language extraction, lowering Cost of Retrieval and reducing wrong answers. It is the structured-data backbone of the quality dimension: a claim either validates against schema.org or it does not. The `sameAs` property ties your entity into the wider knowledge graph, mirroring the Lexicon's `DefinedTerm` pattern.

## How to implement it

1. Pick the schema.org type that matches the page's primary entity (`Article`, `Product`, `Organization`, `FAQPage`, `HowTo`) and fill its required properties.
2. Emit it as a `<script type="application/ld+json">` block in the page head; use an `@graph` when one page carries multiple linked entities, and add `sameAs` to connect identities.
3. Validate with a schema.org-aware validator and confirm the JSON parses and the types resolve.

This satisfies the audit check `quality.json_ld` (pass: at least one valid JSON-LD block validates against schema.org). This page itself ships an `@graph` JSON-LD block as live proof. Use the schema.org vocabulary as the primary source at build, then verify the result with the Agent-Readiness Audit.

Related: [schema.org for agents](/protocols/discovery/schema-org) · [the quality dimension](/agent-readiness/quality) · [the Agentic Web Lexicon](/glossary) · [audit your site](/services)