How to integrate Formbricks MCP with CrewAI

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Introduction

This guide walks you through connecting Formbricks to CrewAI using the Composio tool router. By the end, you'll have a working Formbricks agent that can create a new customer feedback survey, add a contact to our user list, record survey responses from yesterday's event through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Formbricks account through Composio's Formbricks MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Formbricks with

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Formbricks connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Formbricks
  • Build a conversational loop where your agent can execute Formbricks operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

What is the Formbricks MCP server, and what's possible with it?

The Formbricks MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Formbricks account. It provides structured and secure access to your survey management tools, so your agent can perform actions like creating surveys, collecting responses, managing contacts, and handling webhooks automatically on your behalf.

  • Survey creation and management: Easily instruct your agent to create new surveys, define questions, and set up feedback forms tailored to your needs.
  • Automated response collection: Have your agent log responses to surveys, link displays to responses, and streamline data gathering effortlessly.
  • Contact and attribute management: Direct your agent to add or remove contacts, create or delete attribute classes, and segment audiences for more targeted feedback analysis.
  • Webhook configuration for real-time events: Let your agent register new webhooks to automatically send survey response data to external systems or endpoints.
  • Cleanup and maintenance tools: Authorize your agent to delete surveys, survey responses, persons, or unused attributes, keeping your Formbricks workspace organized and up to date.

Supported Tools & Triggers

Tools
Check HealthTool to check the health status of the Formbricks API.
Create Action ClassTool to create a new action class.
Create Attribute ClassCreates a new attribute class (custom contact attribute) in Formbricks.
Create Client UserTool to create or identify a user within a specified environment.
Create ContactCreates a new contact in a Formbricks environment.
Create DisplayCreate a display record to track when a survey is shown to users.
Create Survey ResponseTool to create a response for a survey.
Create SurveyTool to create a new survey.
Create WebhookTool to create a new webhook.
Delete Attribute ClassTool to delete an attribute class.
Delete PersonTool to delete a person.
Delete Survey ResponseTool to delete a survey response by its ID.
Delete SurveyDeletes a survey from Formbricks by its unique identifier.
Delete TeamTool to delete an organization team by its ID.
Delete WebhookTool to delete a webhook by ID.
Get Account InfoRetrieves environment information for the authenticated API key.
Get All ContactsTool to retrieve all contacts within the organization.
Get Attribute ClassTool to get a specific attribute class by ID.
Get Client Contacts StateTool to get the current state of a contact including surveys and segment information.
Get Contact Attribute KeyTool to retrieve detailed information about a specific contact attribute key by ID (v2 API).
Get Contact by IDTool to retrieve a specific contact by its ID.
Get MeTool to retrieve current authenticated organization's and environment details.
Get Person by IDTool to retrieve a person by their internal ID in Formbricks.
Get ResponsesRetrieve survey responses with flexible filtering, sorting, and pagination.
Get RolesTool to retrieve all available roles in the system.
Get WebhookTool to retrieve details of a specific webhook.
List Action ClassesList all action classes in your Formbricks environment.
List Attribute ClassesTool to list all attribute classes.
List Client EnvironmentTool to retrieve environment state for Formbricks SDKs.
List Contact Attribute KeysTool to retrieve contact attribute keys from Formbricks.
List HealthTool to check the health status of critical application dependencies including database and cache.
List Management Contact AttributesTool to retrieve all contact attributes in the environment.
List Management MeTool to retrieve authenticated user's environment and project information.
List Management PeopleTool to retrieve all people (legacy term for contacts) in the environment.
List Organizations Project TeamsTool to list all project-team assignments for an organization (v2 API only).
List Organization TeamsTool to retrieve all teams in an organization (v2 API).
List SurveysList all surveys in the environment.
List WebhooksList all webhooks configured for the current environment.
Update Contact AttributesTool to update a contact's attributes in Formbricks.
Update Survey ResponseTool to update an existing survey response.
Update SurveyUpdates an existing Formbricks survey with new properties.
Update WebhookTool to update an existing webhook.
Upload Bulk ContactsUpload multiple contacts to a Formbricks environment in bulk (up to 250 per request).
Upload Private FileTool to obtain S3 presigned upload data for a private survey file.
Upload Public FileRetrieves S3 presigned upload URLs and form fields for uploading a public file to Formbricks storage.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Formbricks connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Formbricks via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

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

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")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Formbricks MCP URL

Create a Composio Tool Router session for Formbricks

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["formbricks"])

url = session.mcp.url
What's happening:
  • You create a Formbricks only session through Composio
  • Composio returns an MCP HTTP URL that exposes Formbricks tools

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

Here's the complete code to get you started with Formbricks and CrewAI:

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

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

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_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.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["formbricks"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Formbricks through Composio's Tool Router. The agent can perform Formbricks operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

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FAQ

What are the differences in Tool Router MCP and Formbricks MCP?

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

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI 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 Formbricks tools.

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

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

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