Tripadvisor

What is tripadvisor-mcp?

tripadvisor-mcp is a Model Context Protocol (MCP) server designed to integrate with the Tripadvisor Content API. It allows AI assistants to interact with Tripadvisor data, including location information, user reviews, and photos, through standardized MCP interfaces. This essentially bridges the gap between AI models and Tripadvisor's vast travel-related data.

How to use tripadvisor-mcp?

To use tripadvisor-mcp, you first need to obtain a Tripadvisor Content API key from the Tripadvisor Developer Portal. This key is then configured as an environment variable (TRIPADVISOR_API_KEY). The server can be run directly, or more commonly, integrated into an MCP client like Claude Desktop by adding a server configuration that specifies the command to run the tripadvisor-mcp server. Docker support is also provided for easy deployment, allowing users to build and run the server within a container using docker build and docker run commands, or via docker-compose.

Key Features of tripadvisor-mcp

• Location Search: Search for various locations such as hotels, restaurants, and attractions on Tripadvisor.
• Detailed Location Information: Retrieve comprehensive details about specific locations.
• Reviews and Photos: Access user reviews and photos associated with locations.
• Nearby Location Search: Find locations based on geographical coordinates.
• API Key Authentication: Secure access to the Tripadvisor API using an API key.
• Docker Support: Easy deployment and isolation through Docker containerization.
• Configurable Tools: Provides interactive tools for AI assistants, with the ability to configure which tools are made available to the MCP client.

Use Cases of tripadvisor-mcp

• AI Travel Assistants: Power AI assistants that can help users plan trips, find accommodations, discover restaurants, and explore attractions by providing real-time Tripadvisor data.
• Travel Planning Applications: Integrate Tripadvisor data into travel planning applications to offer richer content and more informed recommendations.
• Data Analysis: Utilize the MCP server to access and analyze Tripadvisor data for research or business intelligence purposes.

FAQ from tripadvisor-mcp

Q: What is the Model Context Protocol (MCP)? A: The Model Context Protocol (MCP) is a standardized interface that allows AI models to interact with external services and data sources, providing them with context and capabilities beyond their core training.

Q: How do I get a Tripadvisor Content API key? A: You can obtain a Tripadvisor Content API key from the Tripadvisor Developer Portal.

Q: Can I run tripadvisor-mcp with Docker? A: Yes, tripadvisor-mcp includes full Docker support, allowing you to build and run the server in a Docker container for easy deployment and management.

Q: What tools does tripadvisor-mcp provide? A: tripadvisor-mcp provides tools for searching locations (search_locations, search_nearby_locations), retrieving details (get_location_details), and accessing reviews and photos (get_location_reviews, get_location_photos). These tools are configurable.

Tripadvisor MCP Server

A Model Context Protocol (MCP) server for Tripadvisor Content API.

This provides access to Tripadvisor location data, reviews, and photos through standardized MCP interfaces, allowing AI assistants to search for travel destinations and experiences.

Features

• Search for locations (hotels, restaurants, attractions) on Tripadvisor

• Get detailed information about specific locations

• Retrieve reviews and photos for locations

• Search for nearby locations based on coordinates

• API Key authentication

• Docker containerization support

• Provide interactive tools for AI assistants

The list of tools is configurable, so you can choose which tools you want to make available to the MCP client.

Usage

1. Get your Tripadvisor Content API key from the Tripadvisor Developer Portal.

2. Configure the environment variables for your Tripadvisor Content API, either through a .env file or system environment variables:

# Required: Tripadvisor Content API configuration
TRIPADVISOR_API_KEY=your_api_key_here

3. Add the server configuration to your client configuration file. For example, for Claude Desktop:

{
  "mcpServers": {
    "tripadvisor": {
      "command": "uv",
      "args": [
        "--directory",
        "<full path to tripadvisor-mcp directory>",
        "run",
        "src/tripadvisor_mcp/main.py"
      ],
      "env": {
        "TRIPADVISOR_API_KEY": "your_api_key_here"
      }
    }
  }
}

Note: if you see Error: spawn uv ENOENT in Claude Desktop, you may need to specify the full path to uv or set the environment variable NO_UV=1 in the configuration.

Docker Usage

This project includes Docker support for easy deployment and isolation.

Building the Docker Image

Build the Docker image using:

docker build -t tripadvisor-mcp-server .

Running with Docker

You can run the server using Docker in several ways:

Using docker run directly:

docker run -it --rm \
  -e TRIPADVISOR_API_KEY=your_api_key_here \
  tripadvisor-mcp-server

Using docker-compose:

Create a .env file with your Tripadvisor API key and then run:

docker-compose up

Running with Docker in Claude Desktop

To use the containerized server with Claude Desktop, update the configuration to use Docker with the environment variables:

{
  "mcpServers": {
    "tripadvisor": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e", "TRIPADVISOR_API_KEY",
        "tripadvisor-mcp-server"
      ],
      "env": {
        "TRIPADVISOR_API_KEY": "your_api_key_here"
      }
    }
  }
}

This configuration passes the environment variables from Claude Desktop to the Docker container by using the -e flag with just the variable name, and providing the actual values in the env object.

Development

Contributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements.

This project uses uv to manage dependencies. Install uv following the instructions for your platform:

curl -LsSf https://astral.sh/uv/install.sh | sh

You can then create a virtual environment and install the dependencies with:

uv venv
source .venv/bin/activate  # On Unix/macOS
.venv\Scripts\activate     # On Windows
uv pip install -e .

Project Structure

The project has been organized with a src directory structure:

tripadvisor-mcp/
├── src/
│   └── tripadvisor_mcp/
│       ├── __init__.py      # Package initialization
│       ├── server.py        # MCP server implementation
│       ├── main.py          # Main application logic
├── Dockerfile               # Docker configuration
├── docker-compose.yml       # Docker Compose configuration
├── .dockerignore            # Docker ignore file
├── pyproject.toml           # Project configuration
└── README.md                # This file

Testing

The project includes a test suite that ensures functionality and helps prevent regressions.

Run the tests with pytest:

# Install development dependencies
uv pip install -e ".[dev]"

# Run the tests
pytest

# Run with coverage report
pytest --cov=src --cov-report=term-missing

Tools

Tool	Category	Description
search_locations	Search	Search for locations by query text, category, and other filters
search_nearby_locations	Search	Find locations near specific coordinates
get_location_details	Retrieval	Get detailed information about a location
get_location_reviews	Retrieval	Retrieve reviews for a location
get_location_photos	Retrieval	Get photos for a location

License

MIT

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