mcdonalds.com — AI Search Visibility Report
Overall score: 68/100
AI search visibility analysis for mcdonalds.com. LLMao scored mcdonalds.com 68/100 across 8 LLM-readiness categories including crawlability, semantic content, structured data, authority signals, and answer-engine clarity.
Analyzed URL
Category breakdown
- readability: 85/100 — Content is highly accessible, using simple language and short sentences suitable for a broad audience.
- schema_markup: 30/100 — Basic ImageObject schema found, but missing critical Restaurant, Menu, and Organization JSON-LD.
- authority_trust: 70/100 — Strong corporate trust signals (contact, terms) but lacks individual author expertise for content.
- citation_sources: 40/100 — Lacks external citations or links to primary data sources for nutritional or sourcing claims.
- content_freshness: 95/100 — Excellent recency with multiple 2026 dates and current promotional cycles.
- content_structure: 70/100 — Good use of sections but lacks a clear H1 and consistent heading nesting.
- entity_definition: 75/100 — Brand identity is very strong and consistent, though Person entities are missing.
- technical_accessibility: 75/100 — Good meta tags and social metadata, but heavy reliance on JS for core menu rendering.
Top recommendations
- Add Menu and Restaurant Schema (Schema Markup): Implement Restaurant and Menu schema to help LLMs parse specific food items, prices, and nutritional data directly.
- Implement Author Bylines (Authority & Trust): Add explicit author bylines and Person schema for corporate news and blog content to improve E-E-A-T.
- Add Authoritative Outbound Links (Citation & Source Quality): Include outbound links to nutritional databases or sourcing partners to verify quality claims.
- Optimize Heading Hierarchy (Content Structure): Fix the heading hierarchy; the page jumps from H2 to H2 without a clear H1 or nested H3s for sub-details.
- Reduce JS Dependency for Content (Technical Accessibility): Ensure all promotional content is available in the HTML source without requiring JavaScript execution for LLM crawlers.