Tripadvisor MCP Server

What does “Tripadvisor” MCP Server do?

The Tripadvisor MCP (Model Context Protocol) Server is a middleware component that connects AI assistants with the Tripadvisor Content API, offering standardized interfaces for accessing rich travel-related data. By leveraging this server, developers can empower AI agents to search for locations (hotels, restaurants, attractions), retrieve detailed information, reviews, and photos, and perform searches based on coordinates. This enhances development workflows by enabling seamless integration of real-world travel data into AI-driven applications, supporting tasks such as destination discovery, trip planning, and more. The server supports API key authentication, Docker deployment, and interactive tools, making it versatile for a range of AI assistants and client platforms.

List of Prompts

No prompt templates are specified in the repository or documentation.

List of Resources

No explicit MCP resources are described in the repository or documentation.

List of Tools

• Search for locations
  Exposes the ability to search for hotels, restaurants, and attractions on Tripadvisor.
• Get detailed location information
  Fetches comprehensive information about a specific location (e.g., hotel, restaurant).
• Retrieve reviews and photos
  Allows retrieval of user reviews and photos for a given Tripadvisor location.
• Search nearby locations
  Enables finding locations near specified coordinates.

Use Cases of this MCP Server

• Destination Search
  Developers can enable AI assistants to search for travel destinations, hotels, restaurants, and attractions using the standardized MCP interface.
• Travel Planning
  Integrate detailed location data, reviews, and photos into AI-driven itinerary or trip planning tools.
• Personalized Recommendations
  Use the server’s search and review retrieval tools to build AI applications that suggest destinations or experiences tailored to user preferences.
• Location-Based Discovery
  Support “what’s nearby” features for users by searching for attractions or amenities based on geographic coordinates.
• Content Aggregation for Travel Apps
  Aggregate and display Tripadvisor data within travel apps or chatbots to provide comprehensive, up-to-date information to end-users.

How to set it up

Windsurf

1. Ensure you have uv installed and your Tripadvisor API key.
2. Clone the repository and locate the tripadvisor-mcp directory.
3. Edit your Windsurf configuration file to add the MCP server:
4. Insert the following JSON under the mcpServers object:

   {
     "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"
       }
     }
   }
   

5. Save the configuration and restart Windsurf.

Claude

1. Obtain your Tripadvisor API key from the Tripadvisor Developer Portal.
2. Clone the repository and find the tripadvisor-mcp directory.
3. In your Claude Desktop configuration, add:

   {
     "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"
         }
       }
     }
   }
   

4. If you encounter an ENOENT error, set the full path to uv or set NO_UV=1.
5. Restart Claude Desktop to apply changes.

Cursor

1. Make sure you have Docker and your Tripadvisor API key ready.
2. Build the Docker image:

   docker build -t tripadvisor-mcp-server .
   

3. Add the server configuration to Cursor’s settings:

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

4. Save and restart Cursor.

Cline

1. Clone the repository and retrieve your Tripadvisor API key.
2. Add the MCP server configuration to Cline’s configuration file:

   {
     "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"
         }
       }
     }
   }
   

3. Save the file and restart Cline.

Securing API Keys

Always use environment variables to store API keys for security. Example configuration:

{
  "env": {
    "TRIPADVISOR_API_KEY": "your_api_key_here"
  },
  "inputs": {
    "api_key": "TRIPADVISOR_API_KEY"
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "tripadvisor": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “tripadvisor” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server is well-scoped and focused on a clear use case (Tripadvisor data), providing essential tools for travel-related AI applications and good instructions for setup and deployment. However, it lacks details on prompt templates, explicit MCP resources, or advanced MCP features like roots and sampling.

Rating: 6/10 — Solid, functional, but with limited MCP-specific depth.

MCP Score