What is MCP server for Tripadvisor Content API providing access to location data, reviews, and photos.?
This Model Context Protocol (MCP) server for Tripadvisor Content API allows access to Tripadvisor location data, reviews, and photos through standardized MCP interfaces, enabling AI assistants to search for travel destinations and experiences.
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.
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:
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: