MCP Booking

🚀 Restaurant Booking MCP Server

An AI-powered Model Context Protocol (MCP) server for restaurant discovery and booking. It integrates with Google Maps Places API to find restaurants based on various criteria, offering intelligent recommendations and booking assistance.

🚀 Quick Start

This AI - powered Model Context Protocol (MCP) server simplifies restaurant discovery and booking. It integrates with Google Maps Places API, enabling users to find restaurants according to location, cuisine preferences, mood, and event type, and provides intelligent recommendations and booking support.

✨ Features

🎯 Key Features

• Smart Restaurant Search: Find restaurants within a 20km radius with advanced filtering.
• Default Taiwan Location: Automatically searches around Taiwan (24.1501164, 120.6692299) when no coordinates are specified.
• AI - Powered Recommendations: Get the top 3 restaurant suggestions with detailed reasoning.
• Google Maps Integration: Access real restaurant data including ratings, reviews, and photos.
• Event - Specific Matching: Optimized for dating, family gatherings, business meetings, and celebrations.
• Mood - Based Filtering: Find restaurants matching romantic, casual, upscale, fun, or quiet atmospheres.
• Booking Assistance: Get reservation instructions and mock booking capabilities.

More Features

• 🔍 Smart Restaurant Search: Find restaurants within a 20km radius based on location, cuisine types, mood, and event type.
• 📍 Google Maps Integration: Obtain real restaurant data with ratings, reviews, photos, and contact information.
• 📅 Booking Assistance: Check availability and get reservation instructions.
• 🎯 Event - Specific Matching: Receive optimized recommendations for dating, family gatherings, business meetings, etc.
• 🎭 Mood - Based Filtering: Find restaurants that match your desired atmosphere (romantic, casual, upscale, etc.).

📦 Installation

Prerequisites

• Node.js 18+
• A Google Maps API Key with Places API enabled
• Knowledge of TypeScript for customization

Steps

1. Clone or download this project

   git clone <repository - url>
   cd mcp - restaurant - booking
   

2. Install dependencies

   npm install
   

3. Set up environment variables

   cp .env.example .env
   

   Edit .env and add your Google Maps API key:

   GOOGLE_MAPS_API_KEY=your_actual_api_key_here
   

4. Build the project

   npm run build
   

Getting Google Maps API Key

1. Go to Google Cloud Console.
2. Create a new project or select an existing one.
3. Enable the following APIs:
   • Places API
   • Maps JavaScript API
   • Geolocation API
   • Places API (New)
   • Geocoding API
4. Create credentials (API Key).
5. Restrict the API key to the enabled APIs for security.

💻 Usage Examples

Running the Server

Development mode:

npm run dev

Production mode:

npm start

Running in Docker

To run the MCP Restaurant Booking server in Docker:

# Build the Docker image
docker build -t mcp/booking .

# Run the container on the same network as Redis
docker run --rm -i mcp/booking

Available Tools

1. search_restaurants

Find restaurants based on location, cuisine, mood, and event type.

Parameters:

• latitude (number, optional): Search latitude (default: 24.1501164 - Taiwan).
• longitude (number, optional): Search longitude (default: 120.6692299 - Taiwan).
• placeName (string, optional): Place name to search near (e.g., "New York", "Tokyo", "London"). Alternative to providing latitude/longitude coordinates.
• cuisineTypes (string[]): Array of cuisine preferences.
• mood (string): Desired atmosphere.
• event (string): Type of occasion.
• radius (number, optional): Search radius in meters (default: 20000).
• priceLevel (number, optional): Price preference (1 - 4).

Example with default Taiwan location:

{
  "cuisineTypes": ["Chinese", "Taiwanese"],
  "mood": "casual",
  "event": "family gathering",
  "priceLevel": 2
}

Example with explicit coordinates (Taipei):

{
  "latitude": 25.033,
  "longitude": 121.5654,
  "cuisineTypes": ["Italian", "Mediterranean"],
  "mood": "romantic",
  "event": "dating",
  "radius": 15000,
  "priceLevel": 3
}

Example with place name (New York):

{
  "placeName": "New York, NY",
  "cuisineTypes": ["Italian", "American"],
  "mood": "upscale",
  "event": "business meeting",
  "radius": 10000,
  "priceLevel": 3
}

Example with keyword search for specific food types:

{
  "keyword": "hotpot",
  "mood": "casual",
  "event": "family gathering",
  "radius": 10000
}

2. get_restaurant_details

Get detailed information about a specific restaurant.

Parameters:

• placeId (string): Google Places ID of the restaurant.

3. get_booking_instructions

Get instructions on how to make a reservation.

Parameters:

• placeId (string): Google Places ID of the restaurant.

4. check_availability

Check availability for a reservation (mock implementation).

Parameters:

• placeId (string): Google Places ID.
• dateTime (string): Preferred date/time in ISO format.
• partySize (number): Number of people.

5. make_reservation

Attempt to make a reservation (mock implementation).

Parameters:

• placeId (string): Google Places ID.
• partySize (number): Number of people.
• preferredDateTime (string): ISO format date/time.
• contactName (string): Name for reservation.
• contactPhone (string): Phone number.
• contactEmail (string, optional): Email address.
• specialRequests (string, optional): Special requests.

🔧 Technical Details

How It Works

1. Restaurant Discovery

• Uses Google Places Nearby Search API to find restaurants within the specified radius.
• Filters by cuisine types using keyword matching.
• Retrieves detailed information for each restaurant.

2. AI Recommendation Engine

The recommendation system scores restaurants based on:

• Rating & Reviews (40% weight): Higher ratings and more reviews = better score.
• Review Count (20% weight): More reviews indicate reliability.
• Cuisine Match (20% weight): How well the restaurant cuisine matches preferences.
• Event Suitability (10% weight): Appropriateness for the specified event type.
• Mood Match (10% weight): Atmosphere alignment with the desired mood.

3. Event - Specific Scoring

Different events have different criteria:

• Dating: Prefers mid - to - high - end, romantic cuisines, avoids fast food.
• Family Gathering: Prefers family - friendly, budget - to - mid - range options.
• Business Meeting: Prefers quiet, professional, upscale environments.
• Casual Dining: Flexible criteria, budget - friendly options.
• Celebration: Prefers high - end, special occasion venues.

4. Mood Matching

Analyzes restaurant names, reviews, and characteristics for mood keywords:

• Romantic: intimate, cozy, candlelit, wine.
• Casual: relaxed, friendly, laid - back.
• Upscale: elegant, sophisticated, fine dining.
• Fun: lively, energetic, vibrant.
• Quiet: peaceful, serene, calm.

Development

Project Structure

src/
├── types/           # TypeScript type definitions
├── services/        # Core business logic
│   ├── googleMapsService.ts      # Google Maps API integration
│   ├── restaurantRecommendationService.ts  # AI recommendation engine
│   └── bookingService.ts         # Booking logic (mock)
└── index.ts         # MCP server implementation

Scripts

• npm run build: Compile TypeScript.
• npm run dev: Run in development mode with hot reload.
• npm start: Run the compiled version.
• npm run lint: Run ESLint.
• npm run lint:fix: Fix ESLint issues.

Customization

Adding New Cuisine Types

Edit the cuisineMap in src/services/googleMapsService.ts:

const cuisineMap: { [key: string]: string } = {
  "new_cuisine_type": "Display Name"
  // ... existing mappings
};

Modifying Recommendation Logic

Update scoring algorithms in src/services/restaurantRecommendationService.ts:

• calculateRestaurantScore(): Overall scoring logic.
• calculateEventSuitability(): Event - specific criteria.
• calculateMoodMatch(): Mood matching logic.

Adding New Event Types

1. Update the event enum in src/types/index.ts.
2. Add event criteria in the calculateEventSuitability() method.

Limitations

• Booking: Currently uses a mock implementation. Real booking requires integration with restaurant - specific systems or third - party services like OpenTable.
• API Quotas: Google Places API has usage limits and costs.
• Real - time Data: Restaurant hours and availability may not be real - time.
• Geographic Coverage: Limited to areas covered by Google Places API.

Contributing

1. Fork the repository.
2. Create a feature branch.
3. Make your changes.
4. Add tests if applicable.
5. Submit a pull request.

📄 License

This project is under the MIT License. See the LICENSE file for details.

Support

For issues and questions:

1. Check the Google Maps API documentation.
2. Verify your API key has proper permissions.
3. Check API quotas and billing.
4. Review server logs for error details.

Future Enhancements

• [ ] Real booking system integration (OpenTable, Resy, etc.)
• [ ] User preference learning
• [x] Multi - language support
• [ ] Advanced filtering (dietary restrictions, accessibility)
• [ ] Integration with calendar systems
• [x] Price comparison features
• [ ] Social features (reviews, sharing)

Additional Browser Control

Using Browser MCP

• https://chromewebstore.google.com/detail/browser - mcp - automate - your/bjfgambnhccakkhmkepdoekmckoijdlc
• https://docs.browsermcp.io/setup - server#cursor

Sample

• Prompt: - While searching restaurants, please perform as a professional personal assistant to evaluate the condition I provided, do not ask too many questions for me to choose, pick the best suitable selection for me, check the reservation options and guide how to do the reservation. Also list down the Signature Dishes from that restaurant and the approximate pricing per person. When the booking info has a booking url using an external url, use the MCP browse tool to work and find the reservation steps.
• Can you help me book a restaurant nearby Hong Kong 太平洋廣場? I want to have a date with my wife within a fine - dining at 6 pm. Cost is not a concern and it needs to be romantic.