Go to example tasks
Analyze Chicago Pizza Restaurant Reviews for Market Trends
Created by
Crawler Bros
Actor
Google Maps MCP
Scrape a large volume of reviews from a popular Chicago pizza restaurant to identify common customer sentiment and emerging food trends. This provides valuable
Google Maps MCPcrawlerbros/google-maps-mcp
Record type
Name
Description
Index
+26 fieldsTextNumberBooleanListObject
Input
Scraping Mode(required):reviews
Search Query:pizza restaurant
Location:New York, NY
Maximum Results:5
Place URL:https://www.google.com/maps/place/Lou+Malnati's+Pizzeria/@41.8906927,-87.6293902,17z/data=!3m1!5s0x880e2cb038289417:0x56a5c2d618d45131!4m8!3m7!1s0x880e2cb0383b1a2b:0x5e5b3f7a637a28e9!5m2!4m1!1i2!8m2!3d41.8906734!4d-87.6272015?hl=en
Maximum Reviews:1000
Output fields
Record type
Name
Description
Index
Place id
Feature id
Category name
Categories
Total score
Reviews count
Rating histogram
Address
Plus code
Phone
Phone unformatted
Website
Price level
Hours
Additional info
Photos sample
Menu url
Reserve table url
Reservations url
Order online url
Permanently closed
Temporarily closed
Location
Google maps url
Source url
Scraped at
Sign up on Apify01
Create your Apify account to access the Google Maps MCP.
Start the run02
The Actor will start running based on the input automatically.
Receive the output03
Monitor the progress in real-time. You will be notified as soon as your dataset is complete and ready for review.
Integrate into your workflow04
The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow.
