Restaurant review intelligence as structured JSON

The OpenTable Reviews API turns OpenTable restaurant reviews into clean structured JSON, one row per review. Give it a restaurant and get every review with the full text, the date the diner visited, the date they posted, the diner's public profile, and the complete rating breakdown: overall, food, service, ambience, value, and noise. That six-way score per review is the part no other review site hands you as a field.

Turn on the restaurant summary and each run also returns aggregate ratings, review counts, and the star-by-star histogram per venue. It reads public reviews only, up to 500 per restaurant per run, and never touches reservations or anything behind a diner's private account. The value is the dated series: run it on a schedule and you get a reputation trendline, not a single snapshot.

What each review returns

• content: the full review text

• rating: overall, food, service, ambience, value, and noise

• dined_at and submitted_at: when the visit happened and when the review posted

• user: the reviewer's public name, review count, and location

• review_id and restaurant_id: stable keys for joins and dedupe

Example tasks and use cases

• Get recent OpenTable reviews for one restaurant - pull the latest feedback for a single venue

• Monitor OpenTable reviews for a list of restaurants - batch a whole watchlist in one run

• Monitor San Francisco restaurants - a ready-made Bay Area watchlist

• Monitor Los Angeles restaurants - track LA dining reputation over time

• Monitor Chicago restaurants - a prebuilt Chicago watchlist

• Monitor Houston restaurants - cover the Houston market

• Monitor London restaurants - a prebuilt London watchlist

• Monitor Dubai restaurants - track Dubai venues

• Export OpenTable reviews to CSV - land rows in a CSV a GM will read

• Export OpenTable reviews to a spreadsheet - the same rows in a spreadsheet

• Analyze OpenTable reviews for sentiment analysis - the dataset shape for NLP work

Works with MCP

The Actor is MCP-ready, so through Apify's MCP server it becomes a tool that Claude, Claude Code, or Cursor can call. Ask for a restaurant's recent reviews and let the model summarize themes across food, service, and value in one pass, no scraping code.

Pricing

Pay per review collected. A 50-review pull costs 50 review events and nothing more. Cap each venue with maxResultsPerRestaurant before the run starts, and new Apify accounts include free platform credit to test with.

Keywords and topics

OpenTable reviews API, restaurant review scraper, hospitality analytics, review monitoring, restaurant reputation tracking, diner ratings JSON, sentiment analysis dataset, restaurant competitive research.