Restaurant Booking MCP Server

An AI-powered Model Context Protocol (MCP) server for restaurant discovery and booking. This server integrates with Google Maps Places API to find restaurants based on location, cuisine preferences, mood, and event type, then provides intelligent recommendations and booking assistance.

🎯 Key Features

Smart Restaurant Search: Find restaurants within 20km radius with advanced filtering
Default Taiwan Location: Automatically searches around Taiwan (24.1501164, 120.6692299) when no coordinates specified
AI-Powered Recommendations: Get top 3 restaurant suggestions with detailed reasoning
Google Maps Integration: 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

Features

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

Prerequisites

Node.js 18+
Google Maps API Key with Places API enabled
TypeScript knowledge for customization

Installation

Clone or download this project

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

Install dependencies
```
npm install
```
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
```
Build the project
```
npm run build
```

Getting Google Maps API Key

Go to Google Cloud Console
Create a new project or select existing one
Enable the following APIs:
- Places API
- Maps JavaScript API
- Geolocation API
- Places API (New)
- Geocoding API
Create credentials (API Key)
Restrict the API key to the enabled APIs for security

Usage

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

The MCP server provides the following 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

How It Works

1. Restaurant Discovery

Uses Google Places Nearby Search API to find restaurants within 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 restaurant cuisine matches preferences
Event Suitability (10% weight): Appropriateness for the specified event type
Mood Match (10% weight): Atmosphere alignment with 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 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

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

Limitations

Booking: Currently uses 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

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

License

MIT License - see LICENSE file for details

Support

For issues and questions:

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

Future Enhancements

Real booking system integration (OpenTable, Resy, etc.)
User preference learning
Multi-language support
Advanced filtering (dietary restrictions, accessibility)
Integration with calendar systems
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 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, checking the reservation options and guide how to do the reservation. also list down the Signature Dishes from that restaurant and Approximately pricing per person. When booking info has booking url using external url, use the mcp browse tool to work and find reservation steps.
can you help me book a restaurant nearby hongkong 太平洋廣場, I want to have a date with my wife within a fine-dining at evening 6pm. cost is not a concern and needs to be romatic

Restaurant Booking

Restaurant Booking MCP Server

🎯 Key Features

Features

Prerequisites

Installation

Getting Google Maps API Key

Usage

Running the Server

Running in Docker

Available Tools

1. search_restaurants

2. get_restaurant_details

3. get_booking_instructions

4. check_availability

5. make_reservation

How It Works

1. Restaurant Discovery

2. AI Recommendation Engine

3. Event-Specific Scoring

4. Mood Matching

Development

Project Structure

Scripts

Customization

Adding New Cuisine Types

Modifying Recommendation Logic

Adding New Event Types

Limitations

Contributing

License

Support

Future Enhancements

Additional Browser Control

Sample

Be the First to Experience MCP Now

1. `search_restaurants`

2. `get_restaurant_details`

3. `get_booking_instructions`

4. `check_availability`

5. `make_reservation`