Schema markup for restaurants: the complete guide

Most local businesses benefit from LocalBusiness schema. But restaurants have access to a much richer set of schema types – ones that directly reflect what people search for when they’re deciding where to eat.

Think about what a diner wants to know before they visit: what kind of food do you serve, what does it cost, when are you open, can they book a table, and what do other people think of it? Schema markup can put all of that information directly into Google’s search results – not buried on your website, but front and centre before they’ve even clicked.

The stats back this up. Rich results receive around 58% of all clicks compared to 41% for standard blue-link results. And with AI-powered search tools now answering restaurant queries directly – pulling details about menus, hours, and bookings without the user needing to visit a website – having your structured data in order has never been more important.

1. Restaurant schema (your foundation)

Restaurant schema is a subtype of LocalBusiness – so it includes everything a local business schema does, plus properties specific to food businesses. This is your foundation. Everything else builds on it.

At a minimum, your Restaurant schema should include:

• Name, address, phone number – your NAP, kept identical across your website, schema, and Google Business Profile
• Opening hours – use openingHoursSpecification for precise, day-by-day accuracy
• Cuisine type – the servesCuisine property (e.g. “Italian”, “Japanese”, “Modern British”)
• Price range – the priceRange property, typically expressed as £, ££, £££ or ££££
• Geo coordinates – latitude and longitude, critical for Google Maps visibility
• Menu link – a URL pointing to your HTML menu page (not a PDF)
• Accepts reservations – a simple true/false, or a URL to your booking page
• Photos – the image property, multiple aspect ratios if possible (1:1, 4:3, 16:9)

One important note: if you have multiple locations, each one should have its own Restaurant schema on its own location page. Don’t try to combine them – search engines and AI tools need to match each set of details to a specific venue.

2. Menu schema – the game-changer for food businesses

This is where restaurant schema gets really interesting, and where most restaurant websites are leaving visibility on the table.

Menu schema lets you mark up your food and drink offerings as structured data, so search engines can understand your menu as organised information – not just text on a page. The schema supports a full hierarchy:

• Menu – the top-level container (e.g. “Lunch Menu”, “Dinner Menu”, “Cocktail Menu”)
• MenuSection – sections within a menu (e.g. “Starters”, “Mains”, “Desserts”, “Small Plates”)
• MenuItem – individual dishes, each with their own properties
• Offer – price, currency, and availability information per item

You can go further still: dietary flags (GlutenFreeDiet, VeganDiet, VegetarianDiet), nutritional information, item descriptions, and photos per dish. The more detail you add, the more useful your data becomes to both search engines and AI tools.

One practical note: your menu page needs to be HTML, not a PDF. AI tools and search engines can’t parse PDFs as structured data. If your menu is currently a PDF download, moving it to a properly designed HTML page is one of the highest-impact changes you can make — both for schema and for usability. We design restaurant menu pages that work well for both.

3. Review schema

AggregateRating schema lets you mark up your overall rating and number of reviews so they appear as star ratings in search results. This is one of the most eye-catching rich results available – and AI search tools weight review sentiment heavily when making restaurant recommendations. Important: you can only mark up reviews that are visible on your own website. You can’t pull in ratings from Google Maps or TripAdvisor.

4. Event schema

If your restaurant runs regular events – a quiz night, a supper club, a wine tasting, a live music evening – Event schema is worth adding. Events can appear directly in Google search results with dates, times, and ticket or booking links. It also gives you a steady stream of fresh structured data, which signals to search engines that your site is actively maintained.

5. FAQ schema

A well-placed FAQ section on your restaurant’s homepage or key pages — with FAQPage schema applied – can expand your search listing significantly and is one of the schema types most frequently cited in AI Overviews. Good FAQ candidates for restaurants:

• Do you take walk-ins or is it reservation only?
• Do you cater for dietary requirements?
• Is there parking nearby?
• Do you offer private dining?
• What is your cancellation policy?

These are exactly the questions people ask before deciding to visit – and exactly the kind of content AI search tools pull into direct answers.

AI-powered search tools – Google’s AI Overviews, Bing Copilot, and voice assistants like Siri and Alexa — are increasingly the first place people get restaurant recommendations. When someone asks “good Italian restaurant near me open tonight”, an AI tool is trying to pull together name, cuisine, location, hours, reviews, and booking options in one answer.

Schema markup gives AI systems a machine-readable source of truth for all of that. Without it, they’re inferring your details from unstructured text – which means they might get them wrong, miss them entirely, or default to a competitor whose data is cleaner.

The things AI tools most reliably pull from restaurant schema: your name and address, opening hours, cuisine type, price range, menu link, whether you take reservations, and FAQ answers. Getting these right and keeping them current is the single most impactful thing you can do for AI search visibility as a food business.

One stat worth knowing: 62% of consumers say they would avoid a business if they found incorrect information online. That’s not just a reputation risk – it’s a schema accuracy issue. If your schema says you’re open on Mondays but you’re not, AI tools will confidently tell people the wrong thing.

• Using a PDF for your menu. Search engines and AI tools can’t read PDFs as structured data. Move your menu to HTML.
• Inconsistent NAP. Your name, address, and phone number must match exactly across your website, schema, and Google Business Profile. Discrepancies confuse search engines and AI tools.
• One schema for multiple locations. Each venue needs its own Restaurant entity on its own location page.
• Forgetting to update. Schema isn’t set-and-forget. When your hours change, your menu changes, or you stop taking reservations, your schema needs to update too.
• Marking up content that isn’t on the page. Google’s guidelines are clear: only mark up content that’s visible to visitors. If a dish isn’t on your published menu page, don’t include it in your schema.

Google Rich Results Test – paste your URL or code to see which rich results your page is eligible for. Start here.

Schema Markup Validator – more thorough for catching structural issues in your markup. Use this alongside the Rich Results Test.

Google Search Console – once your schema is live, the Enhancements section will show you which pages have valid structured data and flag any errors Google finds while crawling.

For reference, here’s a simplified example of Restaurant schema in JSON-LD format – the format Google recommends. In practice this would sit in the <head> section of your page:

In a WordPress site, a plugin like Rank Math or Yoast can generate the basics automatically. But for Menu schema, Event schema, and properly nested FAQs, manual JSON-LD is almost always more accurate – and more likely to trigger rich results.