Tripadvisor Mcp Server

What is Tripadvisor MCP?

Tripadvisor MCP (Model Context Protocol) is a server designed to provide access to the Tripadvisor Content API. This innovative platform allows developers and AI assistants to retrieve location data, reviews, and photos from Tripadvisor through standardized MCP interfaces. By utilizing Tripadvisor MCP, users can efficiently search for travel destinations and experiences, making it an essential tool for travel-related applications.

Features of Tripadvisor MCP

• Access to Rich Content: Users can access a wealth of information including location data, user reviews, and high-quality photos from Tripadvisor.
• Standardized Interfaces: The MCP interfaces provide a consistent way to interact with the Tripadvisor data, simplifying the integration process for developers.
• AI Compatibility: Designed with AI assistants in mind, Tripadvisor MCP allows for seamless integration into various applications, enhancing user experience in travel planning.
• Public Repository: The project is publicly available, encouraging collaboration and contributions from developers around the world.

How to Use Tripadvisor MCP

1. Set Up Your Environment: Ensure you have the necessary tools and libraries to interact with the Tripadvisor Content API.
2. Access the API: Utilize the standardized MCP interfaces to connect to the Tripadvisor data. This may involve authentication and setting up API keys.
3. Query for Data: Use the provided endpoints to search for travel destinations, retrieve reviews, and access photos.
4. Integrate into Your Application: Incorporate the retrieved data into your application, whether it be a travel planning tool, a chatbot, or any other AI-driven service.

Frequently Asked Questions

Q: What kind of data can I access through Tripadvisor MCP?

A: You can access location data, user reviews, and photos related to various travel destinations.

Q: Is Tripadvisor MCP free to use?

A: Yes, the Tripadvisor MCP is a public repository, allowing developers to use it without any cost.

Q: Can I contribute to the Tripadvisor MCP project?

A: Absolutely! Contributions are welcome, and you can collaborate with other developers to enhance the project.

Q: How do I report issues or request features?

A: You can report issues or request new features by creating an issue in the project's GitHub repository.

Q: Is there documentation available for Tripadvisor MCP?

A: Yes, comprehensive documentation is typically provided within the repository, detailing how to use the API and its features.

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