# How to integrate Tripadvisor content api MCP with LangChain

```json
{
  "title": "How to integrate Tripadvisor content api MCP with LangChain",
  "toolkit": "Tripadvisor content api",
  "toolkit_slug": "tripadvisor_content_api",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/tripadvisor_content_api/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/tripadvisor_content_api/framework/langchain.md",
  "updated_at": "2026-05-12T10:29:06.856Z"
}
```

## Introduction

This guide walks you through connecting Tripadvisor content api to LangChain using the Composio tool router. By the end, you'll have a working Tripadvisor content api agent that can find top-rated restaurants in rome, show recent photos of eiffel tower, list hotels near times square nyc through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Tripadvisor content api account through Composio's Tripadvisor content api MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Tripadvisor content api with

- [OpenAI Agents SDK](https://composio.dev/toolkits/tripadvisor_content_api/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/tripadvisor_content_api/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/tripadvisor_content_api/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/tripadvisor_content_api/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/tripadvisor_content_api/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/tripadvisor_content_api/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/tripadvisor_content_api/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/tripadvisor_content_api/framework/cli)
- [Google ADK](https://composio.dev/toolkits/tripadvisor_content_api/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/tripadvisor_content_api/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/tripadvisor_content_api/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/tripadvisor_content_api/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/tripadvisor_content_api/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Tripadvisor content api project to Composio
- Create a Tool Router MCP session for Tripadvisor content api
- Initialize an MCP client and retrieve Tripadvisor content api tools
- Build a LangChain agent that can interact with Tripadvisor content api
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## What is the Tripadvisor content api MCP server, and what's possible with it?

The Tripadvisor content api MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Tripadvisor content api account. It provides structured and secure access to Tripadvisor’s massive travel data, so your agent can search destinations, retrieve location details, and fetch high-quality photos from the world’s largest travel platform on your behalf.
- Location search and discovery: Instantly have your agent find detailed information on hotels, attractions, restaurants, and more by name or keyword.
- Photo retrieval for destinations: Fetch recent, high-quality images for any Tripadvisor location to enhance travel recommendations or trip planning experiences.
- Multi-language data access: Access content and reviews in up to 29 languages, making it easy to build global-ready applications and assistants.
- Trip planning assistance: Enable your agent to suggest popular spots, review ratings, and gather visual inspiration for any itinerary or travel question.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `TRIPADVISOR_CONTENT_API_GET_LOCATION_DETAILS` | Get Location Details | Tool to get comprehensive information about a location (hotel, restaurant, or attraction) including name, address, rating, and contact details. Use when you need detailed information about a specific TripAdvisor location. |
| `TRIPADVISOR_CONTENT_API_GET_LOCATION_PHOTOS_V2` | Get Location Photos (Enhanced) | Tool to retrieve up to 5 high-quality photos for a specific location with complete metadata and pagination support. Use when you need detailed photo information including user details, source filtering, and pagination. Photos are ordered by recency. |
| `TRIPADVISOR_CONTENT_API_GET_LOCATION_REVIEWS` | Get Location Reviews | Tool to retrieve up to 5 of the most recent reviews for a specific location. Use when you need customer feedback, ratings, or review content for a TripAdvisor location. |
| `TRIPADVISOR_CONTENT_API_SEARCH_LOCATIONS2` | Search Locations (Advanced) | Tool to search for TripAdvisor locations with advanced filtering options. Use when you need to find locations by name, category, phone, address, or coordinates with radius-based filtering. |
| `TRIPADVISOR_CONTENT_API_SEARCH_NEARBY_LOCATIONS` | Search Nearby Locations | Tool to search for locations near a specified latitude/longitude. Returns up to 10 locations with optional filtering by category, phone, or address. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Tripadvisor content api MCP server is an implementation of the Model Context Protocol that connects your AI agent to Tripadvisor content api. It provides structured and secure access so your agent can perform Tripadvisor content api operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to Tripadvisor content api tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Tripadvisor content api tools
- The create method takes the user ID and specifies which toolkits should be available
- The returned session.mcp.url is the MCP server URL that your agent will use
- This approach allows the agent to dynamically load and use Tripadvisor content api tools as needed
```python
# Create Tool Router session for Tripadvisor content api
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['tripadvisor_content_api']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['tripadvisor_content_api']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "tripadvisor_content_api-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "tripadvisor_content_api-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Tripadvisor content api related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Tripadvisor content api related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['tripadvisor_content_api']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "tripadvisor_content_api-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Tripadvisor content api related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['tripadvisor_content_api']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "tripadvisor_content_api-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Tripadvisor content api related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Conclusion

You've successfully built a LangChain agent that can interact with Tripadvisor content api through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## How to build Tripadvisor content api MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/tripadvisor_content_api/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/tripadvisor_content_api/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/tripadvisor_content_api/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/tripadvisor_content_api/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/tripadvisor_content_api/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/tripadvisor_content_api/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/tripadvisor_content_api/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/tripadvisor_content_api/framework/cli)
- [Google ADK](https://composio.dev/toolkits/tripadvisor_content_api/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/tripadvisor_content_api/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/tripadvisor_content_api/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/tripadvisor_content_api/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/tripadvisor_content_api/framework/crew-ai)

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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Tripadvisor content api MCP?

With a standalone Tripadvisor content api MCP server, the agents and LLMs can only access a fixed set of Tripadvisor content api tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Tripadvisor content api and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Tripadvisor content api tools.

### Can I manage the permissions and scopes for Tripadvisor content api while using Tool Router?

Yes, absolutely. You can configure which Tripadvisor content api scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Tripadvisor content api data and credentials are handled as safely as possible.

---
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