# How to integrate Survey monkey MCP with LlamaIndex

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

## Introduction

This guide walks you through connecting Survey monkey to LlamaIndex 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 LlamaIndex 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)
- [LangChain](https://composio.dev/toolkits/survey_monkey/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/survey_monkey/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/survey_monkey/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/survey_monkey/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Set your OpenAI and Composio API keys
- Install LlamaIndex and Composio packages
- Create a Composio Tool Router session for Survey monkey
- Connect LlamaIndex to the Survey monkey MCP server
- Build a Survey monkey-powered agent using LlamaIndex
- Interact with Survey monkey through natural language

## What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.
Key features include:
- ReAct Agent: Reasoning and acting pattern for tool-using agents
- MCP Tools: Native support for Model Context Protocol
- Context Management: Maintain conversation context across interactions
- Async Support: Built for async/await patterns

## 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

Before you begin, make sure you have:
- Python 3.8/Node 16 or higher installed
- A Composio account with the API key
- An OpenAI API key
- A Survey monkey account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Survey monkey

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

```typescript
npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv
```

### 3. Set environment variables

Create a .env file in your project root:
These credentials will be used to:
- Authenticate with OpenAI's GPT-5 model
- Connect to Composio's Tool Router
- Identify your Composio user session for Survey monkey access
```bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id
```

### 4. Import modules

No description provided.
```python
import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")
```

```typescript
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");
```

### 6. Create a Tool Router session and build the agent function

What's happening here:
- We create a Composio client using your API key and configure it with the LlamaIndex provider
- We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, survey monkey)
- The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
- LlamaIndex will connect to this endpoint to dynamically discover and use the available Survey monkey tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["survey_monkey"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Survey monkey actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Survey monkey actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)
```

```typescript
async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["survey_monkey"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Survey monkey actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")
```

```typescript
async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}
```

### 8. Define the main entry point

What's happening here:
- We're orchestrating the entire application flow
- The agent gets built with proper error handling
- Then we kick off the interactive chat loop so you can start talking to Survey monkey
```python
async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Survey monkey, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["survey_monkey"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Survey monkey actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Survey monkey actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["survey_monkey"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Survey monkey actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();
```

## Conclusion

You've successfully connected Survey monkey to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Survey monkey tools through an MCP endpoint
- LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
- The agent becomes more capable without increasing prompt size
- Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

## 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)
- [LangChain](https://composio.dev/toolkits/survey_monkey/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/survey_monkey/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/survey_monkey/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/survey_monkey/framework/crew-ai)

## Related Toolkits

- [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management.
- [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters.
- [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions.
- [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows.
- [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations.
- [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements.
- [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints.
- [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 LlamaIndex?

Yes, you can. LlamaIndex 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.

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