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Tripadvisor MCP Server

Tripadvisor MCP Server

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github.com/pab1it0Media & Design64Updated 83w ago

About Tripadvisor MCP Server

Tripadvisor MCP Server is an MCP server for the Tripadvisor Content API, giving standardized access to location data, reviews and photos. It is intended for developers who want AI assistants to search travel destinations and experiences.

Key features

  1. 01Search hotels, restaurants and attractions
  2. 02Get detailed information for a location
  3. 03Retrieve reviews and photos for locations
  4. 04Find locations near given coordinates
  5. 05API Key authentication
  6. 06Docker containerization support

Use cases

  1. 01Letting an AI assistant suggest nearby restaurants or attractions
  2. 02Supplying reviews and photos to travel planning applications
  3. 03Deploying a travel data lookup service in a container

MCP support

The project runs as an MCP server that exposes search, details, reviews and photo tools to MCP clients. The set of exposed tools is configurable in the client.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"tripadvisor": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"build"
]
}
}
}
Run commanddocker run -i --rm build

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
ocibuild

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

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
  1. 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
ToolCategoryDescription
search_locationsSearchSearch for locations by query text, category, and other filters
search_nearby_locationsSearchFind locations near specific coordinates
get_location_detailsRetrievalGet detailed information about a location
get_location_reviewsRetrievalRetrieve reviews for a location
get_location_photosRetrievalGet photos for a location

License

MIT


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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.