Google Maps MCP Server

A unified Apify MCP (Model Context Protocol) server for comprehensive Google Maps scraping. This actor provides a single interface to search for businesses and scrape reviews using browser automation with Playwright.

🚀 Features

Multi-Mode Scraping

This MCP server supports two scraping modes:

1. Search Mode - Search and scrape business listings from Google Maps
2. Reviews Mode - Scrape reviews from a specific Google Maps business

Key Capabilities

✅ Unified Interface - Single actor for all Google Maps scraping needs ✅ Browser Automation - Reliable scraping using Playwright ✅ No API Key Required - Scrape public content without authentication ✅ Comprehensive Data - Extract all relevant business details and reviews ✅ Automatic Pagination - Load multiple results and reviews automatically ✅ Structured Output - Clean JSON data ready for AI consumption

📋 Input Parameters

Common Parameters

Parameter	Type	Required	Description
mode	string	Yes	Scraping mode: search or reviews

Search Mode Parameters

Parameter	Type	Default	Description
searchQuery	string	-	What to search for (e.g., "restaurant")
location	string	""	Where to search (e.g., "New York, NY")
maxResults	integer	20	Maximum number of businesses to scrape (1-100)

Reviews Mode Parameters

Parameter	Type	Default	Description
placeUrl	string	-	Google Maps place URL
maxReviews	integer	50	Maximum number of reviews to scrape (1-1000)

📝 Input Examples

Example 1: Search for Businesses

{
  "mode": "search",
  "searchQuery": "pizza restaurant",
  "location": "New York, NY",
  "maxResults": 20
}

Example 2: Scrape Reviews

{
  "mode": "reviews",
  "placeUrl": "https://www.google.com/maps/place/Joe's+Pizza/@40.7308314,-73.9973325,17z",
  "maxReviews": 100
}

📊 Output Format

Search Mode Output

Each business includes:

{
  "index": 1,
  "name": "Joe's Pizza",
  "category": "Pizza restaurant",
  "rating": 4.5,
  "review_count": 1234,
  "price_level": "$$",
  "address": "7 Carmine St, New York, NY 10014",
  "phone": "+1 212-366-1182",
  "website": "https://www.joespizzanyc.com",
  "url": "https://www.google.com/maps/place/Joe's+Pizza/@40.7308314,-73.9973325,17z",
  "place_id": "ChIJxxx...",
  "latitude": 40.7308314,
  "longitude": -73.9973325,
  "scraped_at": "2025-11-02T20:30:00"
}

Reviews Mode Output (Flattened Format)

One review per row for easy analysis - each dataset item represents a single review with place metadata:

{
  "place_metadata": {
    "place_url": "https://www.google.com/maps/place/...",
    "scraped_at": "2025-11-02T20:30:00",
    "business_name": "Joe's Pizza",
    "rating": 4.5,
    "total_reviews": 1234,
    "category": "Pizza restaurant",
    "address": "7 Carmine St, New York, NY 10014"
  },
  "review_id": "ChZDSUhNMG9nS0VJQ0FnSUQ...",
  "reviewer_name": "John Smith",
  "reviewer_avatar": "https://lh3.googleusercontent.com/...",
  "rating": 5.0,
  "review_text": "Best pizza in NYC! The crust is perfect and the sauce is amazing...",
  "review_date": "2 months ago",
  "likes": 42
}

Benefits of Flattened Format:

• ✅ Each row is one review (perfect for CSV export and data analysis)
• ✅ Easy to query, filter, and aggregate in databases
• ✅ Compatible with pandas DataFrames and SQL tables
• ✅ Place metadata included in every row (no joins needed)
• ✅ Simplified data pipeline integration

🎯 Use Cases

Business Intelligence

• Market Research - Analyze competitor locations and ratings
• Location Planning - Find optimal areas for new business locations
• Competitive Analysis - Track competitor reviews and ratings
• Customer Insights - Understand what customers value in your industry

Data Analysis & Research

• Sentiment Analysis - Analyze customer sentiment from reviews
• Trend Detection - Identify popular locations and emerging trends
• Service Quality - Compare service quality across locations
• Price Analysis - Study pricing patterns across regions

AI & ML Applications

• Training Data - Build datasets for recommendation systems
• RAG Systems - Feed business and review data to AI models
• Chatbot Training - Use reviews for customer service bots
• Content Generation - Analyze successful business descriptions

🛠️ Local Development

Prerequisites

pip install -r requirements.txt
playwright install chromium

Create Input File

Create storage/key_value_stores/default/INPUT.json:

For Search Mode:

{
  "mode": "search",
  "searchQuery": "coffee shop",
  "location": "San Francisco, CA",
  "maxResults": 10
}

For Reviews Mode:

{
  "mode": "reviews",
  "placeUrl": "https://www.google.com/maps/place/Blue+Bottle+Coffee/@37.7749295,-122.4194155,17z",
  "maxReviews": 50
}

Run Locally

cd Google/mcp
apify run

Check Results

Results are saved in:

• storage/datasets/default/ - Individual records
• storage/key_value_stores/default/OUTPUT.json - Complete output

🚀 Deployment

Using Apify CLI

# Login to Apify
apify login

# Push to Apify platform
apify push

Manual Upload

1. Create a new actor on Apify Console
2. Upload all files including Dockerfile, requirements.txt, and .actor/ directory
3. Configure input parameters
4. Run the actor

📚 API Integration

JavaScript/Node.js

const { ApifyClient } = require("apify-client");

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

// Search for businesses
const searchInput = {
  mode: "search",
  searchQuery: "sushi restaurant",
  location: "Los Angeles, CA",
  maxResults: 25
};

const run = await client.actor("YOUR_ACTOR_ID").call(searchInput);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log(`Found ${items.length} businesses`);

Python

from apify_client import ApifyClient

client = ApifyClient('YOUR_API_TOKEN')

# Scrape reviews
reviews_input = {
    'mode': 'reviews',
    'placeUrl': 'https://www.google.com/maps/place/...',
    'maxReviews': 100
}

run = client.actor('YOUR_ACTOR_ID').call(run_input=reviews_input)

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(f"Review: {item['review_text']}")
    print(f"Rating: {item['rating']}")

⚡ Performance Tips

Optimize Speed

• Start with lower maxResults/maxReviews for testing
• Use specific search queries for better results
• Limit location scope for faster searches
• Process fewer businesses per run for faster completion

Best Practices

• Add delays between requests (built-in)
• Don't scrape the same content repeatedly
• Respect Google's servers - use reasonable limits
• Consider batching requests across multiple runs

⚠️ Limitations

• Public Content Only - Cannot access restricted or private data
• No Authentication - Requires public access to content
• Rate Limits - Google may throttle excessive requests
• Browser-Based - Slower than direct API but more reliable
• Dynamic Content - Some features may change if Google updates layout

🐛 Troubleshooting

No Results Returned

• Verify search query and location are correct
• Check if the place URL is valid and accessible
• Try with smaller maxResults/maxReviews values first
• Review logs for specific error messages

Timeout Errors

• Content may be loading slowly
• Try with fewer items or smaller limits
• Check if Google Maps is accessible from your location

Missing Data Fields

• Some fields may be null if not available
• Not all businesses have all information
• Reviews may vary in completeness

📄 License

This actor is provided as-is for scraping public Google Maps data in accordance with Google's terms of service.

🔗 Related Actors

• ../google-maps/ - Dedicated business search scraper
• ../google-maps-reviews/ - Dedicated reviews scraper

💡 Notes

• This MCP server uses browser automation to access Google Maps public interface
• Always respect Google's robots.txt and terms of service
• Use responsibly and avoid overwhelming Google's servers
• Consider implementing additional rate limiting for large-scale scraping
• The actor works best with the Apify platform's infrastructure

🆘 Support

For issues, questions, or feature requests, please open an issue in the repository or contact support.

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