# How to integrate Survey monkey MCP with LangChain

```json
{
  "title": "How to integrate Survey monkey MCP with LangChain",
  "toolkit": "Survey monkey",
  "toolkit_slug": "survey_monkey",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/survey_monkey/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/survey_monkey/framework/langchain.md",
  "updated_at": "2026-05-12T10:27:39.560Z"
}
```

## Introduction

This guide walks you through connecting Survey monkey to LangChain using the Composio tool router. By the end, you'll have a working Survey monkey agent that can create a survey titled 'employee feedback', list all surveys from last month, get responses for the 'customer satisfaction' survey through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Survey monkey account through Composio's Survey monkey MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Survey monkey with

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

## TL;DR

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

The Survey monkey MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your SurveyMonkey account. It provides structured and secure access to your surveys and data, so your agent can create surveys, distribute them, analyze responses, and manage contacts on your behalf.
- Survey creation and management: Quickly instruct your agent to create new surveys for any purpose or delete surveys you no longer need.
- Survey distribution control: Retrieve and manage collector links and distribution channels so your agent can help you share surveys with the right people.
- Real-time response analysis: Fetch detailed survey responses and metadata, enabling your agent to analyze feedback and generate insights instantly.
- Contact and group coordination: Access and manage your SurveyMonkey contacts and groups, letting your agent organize recipients and streamline survey delivery.
- Survey inventory and details lookup: List all your surveys or fetch specific details and counts for any survey, making it easy for your agent to keep you up-to-date.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SURVEY_MONKEY_CREATE_BULK_CONTACTS` | Create Bulk Contacts | Creates multiple contacts in SurveyMonkey in a single API call. Use this action to efficiently add multiple contacts at once, optionally updating existing ones. Each contact requires first_name, last_name, and either email or phone_number. The response indicates which contacts succeeded, which were invalid, and which already existed. Requires 'contacts_write' OAuth scope. |
| `SURVEY_MONKEY_CREATE_CONTACT` | Create Contact | Creates a new contact in SurveyMonkey. Contacts can be added to contact lists and used for email invitations. Use this action when you need to add a new contact to your SurveyMonkey account for survey distribution. |
| `SURVEY_MONKEY_CREATE_CONTACT_LIST` | Create Contact List | Creates a new contact list in SurveyMonkey. Contact lists are used to organize contacts for sending survey invitations via email or SMS collectors. Use this action when you need to create a contact list before adding contacts and sending surveys. Returns the contact list ID and API URL for managing the list. |
| `SURVEY_MONKEY_CREATE_SURVEY` | Create Survey | Creates a new empty survey in SurveyMonkey with one empty page and no questions. Returns the survey ID and internal URLs for editing, previewing, and analyzing results — shareable collector URLs are not returned; use SURVEY_MONKEY_GET_COLLECTORS after creation to retrieve or manage those. The survey_id can be used with other actions to add questions, pages, or collectors. Finalize survey design before broad distribution, as modifying questions after distributing live links can invalidate prior responses. Example: "Create a survey titled 'Customer Satisfaction Survey'" |
| `SURVEY_MONKEY_CREATE_SURVEY_FOLDER` | Create Survey Folder | Creates a new survey folder in SurveyMonkey to organize surveys. Use when you need to create a folder for grouping related surveys. |
| `SURVEY_MONKEY_DELETE_SURVEY` | Delete Survey | Tool to delete a specific survey. Use when the survey ID is confirmed correct. Deletion is irreversible. Example prompt: "Delete survey '123456789'." |
| `SURVEY_MONKEY_GET_BULK_CONTACTS` | Bulk Get Contacts | Tool to retrieve contacts in bulk from SurveyMonkey. Use when you need to fetch multiple contacts efficiently with pagination support. |
| `SURVEY_MONKEY_GET_COLLECTORS` | Get Survey Collectors | Tool to retrieve a list of collectors for a specific survey. Use when you need collector URLs, counts, and statuses. Survey creation does not return shareable links; use this tool to obtain collector URLs after creating a survey. |
| `SURVEY_MONKEY_GET_CONTACTS` | Get Contacts | Retrieves a list of contacts from SurveyMonkey. Use this tool to fetch contacts that can be used for sending survey invitations. Contacts can be filtered by status (active, optout, bounced), searched by email or name, sorted by various fields, and paginated through using page/per_page parameters. Returns contact details including ID, email, names, phone numbers, and custom fields. Requires 'contacts_read' or 'contacts_write' OAuth scope. |
| `SURVEY_MONKEY_GET_CURRENT_USER` | Get Current User | Tool to retrieve the current authenticated user's account details including plan information. Use when you need to get information about the authenticated user's SurveyMonkey account. |
| `SURVEY_MONKEY_GET_GROUPS` | Get Groups | Tool to retrieve a list of groups. Use after authentication when you need to enumerate or paginate through all groups in your SurveyMonkey account. |
| `SURVEY_MONKEY_GET_RESPONSES` | Get Survey Responses | Tool to retrieve a paginated list of responses for a specific survey. Use when you need to browse or filter responses after confirming the survey ID. Iterate through all pages using `page` and `per_page` to avoid missing responses in large surveys. |
| `SURVEY_MONKEY_GET_SURVEY_DETAILS` | Get Survey Details | Retrieves comprehensive details and metadata for a specific survey by its ID. Returns survey configuration including title, language, question/page counts, response count, URLs for preview/edit/analyze/collect, navigation button text, and creation/modification timestamps. Use this to get detailed information about a survey after obtaining its ID from Get Surveys. |
| `SURVEY_MONKEY_GET_SURVEY_DETAILS2` | Get Survey Details (Expanded) | Retrieves expanded survey details including all pages, questions, and answer options. Use when you need the complete survey structure with question IDs and answer option IDs for mapping responses. |
| `SURVEY_MONKEY_GET_SURVEY_RESPONSES_BULK` | Get Survey Responses (Bulk) | Tool to retrieve bulk survey responses with full question answers and response data. Use when you need to export or analyze detailed response data for a survey. |
| `SURVEY_MONKEY_GET_SURVEYS` | Get Surveys | Tool to retrieve a paginated list of surveys. Use when you need to enumerate or paginate through all surveys. Results are capped at 100 per page (`per_page` max=100); iterate over all pages using `page` to avoid missing surveys on large accounts. |
| `SURVEY_MONKEY_GET_SURVEY_TRENDS` | Get Survey Trends | Tool to retrieve trend data for a survey showing answer counts for particular time periods. Use when you need to analyze response trends over time for survey questions. Not available for file_upload, slider, presentation, demographic, matrix_menu, or datetime question types. |
| `SURVEY_MONKEY_LIST_AVAILABLE_LANGUAGES` | List Available Languages | Tool to retrieve all available languages for creating multilingual surveys. Use when you need to get language codes and names for survey creation or translation. |
| `SURVEY_MONKEY_LIST_BENCHMARK_BUNDLES` | List Benchmark Bundles | Tool to retrieve a list of benchmark bundles. Use when you need to enumerate available benchmark bundles for benchmarking survey results. |
| `SURVEY_MONKEY_LIST_CONTACT_FIELDS` | List Contact Fields | Tool to retrieve a list of contact fields from SurveyMonkey. Use when you need to enumerate available contact fields that can be used for contact management and data collection. |
| `SURVEY_MONKEY_LIST_CONTACT_LISTS` | List Contact Lists | Tool to retrieve a list of contact lists from SurveyMonkey. Use this when you need to enumerate all contact lists in your account or find a specific list by name. Contact lists are collections of contacts that can be used for sending survey invitations. |
| `SURVEY_MONKEY_LIST_WEBHOOKS` | List Webhooks | Tool to retrieve a list of webhooks from SurveyMonkey. Use when you need to view all configured webhooks or find a specific webhook by name. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "survey_monkey-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({
    "survey_monkey-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 Survey monkey 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 Survey monkey 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=['survey_monkey']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "survey_monkey-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 Survey monkey 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: ['survey_monkey']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "survey_monkey-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 Survey monkey 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 Survey monkey 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 Survey monkey MCP Agent with another framework

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

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- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
- [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation.
- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Survey monkey MCP?

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

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

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

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