# How to integrate Mopinion MCP with LangChain

```json
{
  "title": "How to integrate Mopinion MCP with LangChain",
  "toolkit": "Mopinion",
  "toolkit_slug": "mopinion",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/mopinion/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/mopinion/framework/langchain.md",
  "updated_at": "2026-05-12T10:19:29.427Z"
}
```

## Introduction

This guide walks you through connecting Mopinion to LangChain using the Composio tool router. By the end, you'll have a working Mopinion agent that can get all recent website feedback reports, summarize negative feedback from mobile users, list most common survey responses this week through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Mopinion account through Composio's Mopinion MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Mopinion with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Mopinion project to Composio
- Create a Tool Router MCP session for Mopinion
- Initialize an MCP client and retrieve Mopinion tools
- Build a LangChain agent that can interact with Mopinion
- 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 Mopinion MCP server, and what's possible with it?

The Mopinion MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mopinion account. It provides structured and secure access to your feedback data and analytics, so your agent can perform actions like retrieving user feedback, analyzing survey results, generating reports, and monitoring trends on your behalf.
- Centralized feedback retrieval: Instantly pull user feedback from all your websites and apps so your agent can surface insights across every touchpoint.
- Survey results analysis: Let your agent analyze form and survey submissions, identifying common issues, sentiment, and emerging trends.
- Custom report generation: Have the agent generate detailed feedback reports, aggregating user responses and highlighting actionable improvements.
- Trend and KPI monitoring: Ask your agent to track changes in user sentiment or conversion-related metrics over time for continuous optimization.
- Segmented feedback filtering: Enable your agent to filter and group feedback by device, location, or other custom attributes to uncover targeted opportunities.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `MOPINION_GET_ACCOUNT` | Get Account | Tool to retrieve the current authenticated account information. Use to verify authentication and get account details. |
| `MOPINION_GET_DATASET_BY_ID` | Get Dataset by ID | Retrieve complete metadata for a specific Mopinion dataset (feedback form) by its ID. Returns dataset properties including name, associated report ID, data source type, and description. Use this action when you need detailed information about a specific dataset/form. To discover available dataset IDs, use the List Datasets action first. This is a read-only operation that does not modify any data. |
| `MOPINION_GET_DATASET_FEEDBACK` | Get Dataset Feedback | Retrieve feedback items (survey responses) for a specific dataset/form with optional pagination and filtering. Use this action to fetch multiple feedback entries from a dataset. Each feedback item contains the survey responses including custom fields, scores, metadata, and timestamps. Supports pagination for large result sets and filtering by field values. For retrieving a single feedback item by ID, use GET_DATASET_FEEDBACK_BY_ID instead. |
| `MOPINION_GET_DATASET_FEEDBACK_BY_ID` | Get Dataset Feedback By ID | Retrieves a single feedback item by its unique identifier from a specific dataset. Use this tool when you need detailed information about a specific feedback item. First obtain the dataset_id using List Datasets, then get feedback_id values using Get Dataset Feedback. **Important Notes:** - Returns a warning in metadata if the feedback item doesn't exist (with null field values) - Returns 404 error if the dataset_id itself is invalid or inaccessible - The response includes feedback fields, tags, scores, timestamps, and associated metadata |
| `MOPINION_GET_DATASET_FIELDS` | Get Dataset Field Definitions | Tool to retrieve field definitions for a dataset. Use when you need the schema of a dataset's fields. |
| `MOPINION_GET_DEPLOYMENT_BY_ID` | Get Deployment by ID | Retrieves detailed configuration for a specific feedback form deployment by its ID. Returns deployment rules, trigger conditions, scheduling settings, target URLs, and active status. Use this to understand when, where, and how a feedback form is displayed to users. |
| `MOPINION_GET_DEPLOYMENTS` | Get Deployments | Tool to list all deployments for the authenticated Mopinion account. Use after setting up authentication. |
| `MOPINION_GET_REPORT_BY_ID` | Get Report By ID | Retrieves detailed information about a specific Mopinion report by its ID. Returns comprehensive report details including name, description, language, creation date, and all associated datasets with their metadata. Use this when you need full information about a specific report. To find available report IDs, use the Get Reports action first. Example use case: Get complete details of report 22511 to understand its datasets and configuration. |
| `MOPINION_GET_REPORT_FEEDBACK` | Get Report Feedback | Tool to retrieve feedback items for a report. Use when you need paginated and filtered feedback entries for analysis. |
| `MOPINION_GET_REPORT_FIELDS` | Get Report Fields | Tool to retrieve field definitions for a specific report. Use when you need the schema of a report's feedback fields before constructing or analyzing forms. |
| `MOPINION_GET_ROOT` | Get API Root | Check Mopinion API availability and get version information. Pings the API health check endpoint (/ping) and returns status code, 'pong' response, and current API version. Use this to verify the API is reachable and operational. |

## Supported Triggers

None listed.

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

The Mopinion MCP server is an implementation of the Model Context Protocol that connects your AI agent to Mopinion. It provides structured and secure access so your agent can perform Mopinion 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 Mopinion 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 Mopinion 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 Mopinion tools as needed
```python
# Create Tool Router session for Mopinion
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['mopinion']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "mopinion-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({
    "mopinion-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 Mopinion 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 Mopinion 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=['mopinion']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "mopinion-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 Mopinion 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: ['mopinion']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "mopinion-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 Mopinion 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 Mopinion 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 Mopinion MCP Agent with another framework

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

## Related Toolkits

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- [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research.
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- [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research.
- [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data.
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- [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently.
- [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries.
- [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business.
- [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Mopinion MCP?

With a standalone Mopinion MCP server, the agents and LLMs can only access a fixed set of Mopinion tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Mopinion 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 Mopinion tools.

### Can I manage the permissions and scopes for Mopinion while using Tool Router?

Yes, absolutely. You can configure which Mopinion 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 Mopinion data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)