How to integrate Retently MCP with CrewAI

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Introduction

This guide walks you through connecting Retently to CrewAI using the Composio tool router. By the end, you'll have a working Retently agent that can list all customer feedback from last week, add 'urgent' tag to negative feedback, get latest nps score for my account, create new customer with email and company through natural language commands.

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

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

TL;DR

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

The Retently MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Retently account. It provides structured and secure access to your customer feedback and survey data, so your agent can perform actions like analyzing feedback, managing customers, organizing survey results, and tagging feedback—completely on your behalf.

  • Automated customer management: Effortlessly create, update, or delete customers in your Retently workspace, ensuring your CRM data stays up to date.
  • Feedback analysis and retrieval: Retrieve recent feedback, pull detailed feedback entries, or get a list of all customer responses for easy sentiment tracking and reporting.
  • Survey and campaign insights: Instantly fetch all your Retently campaigns or get the latest NPS score to stay on top of your customer satisfaction metrics.
  • Feedback organization with tags and topics: Let your agent categorize and organize feedback by adding tags or topics, so you can quickly identify trends and areas for improvement.
  • Advanced customer lookup: Quickly get detailed information about any customer by their unique ID, perfect for personalizing follow-ups or resolving support issues.

Supported Tools & Triggers

Tools
Add Feedback TagsAdd tags to feedback items by providing feedback ids and corresponding tags.
Add Feedback TopicsAdd topics to feedback items by providing feedback ids and corresponding topics.
Create or Update CustomersTool to create new customers or update existing ones by providing their details, including email, name, company, tags, and properties.
Delete CustomersDelete customers from the workspace by providing their unique ids.
Get CampaignsTool to retrieve a list of campaigns associated with the account.
Get Customer By IDTool to retrieve detailed information about a specific customer by their unique id.
Get CustomersRetrieve a list of customers with optional parameters for pagination, sorting, and filtering by email or date range.
Get FeedbackTool to retrieve feedback received from customers.
Get Feedback by IDTool to retrieve detailed information about specific feedback by its unique id.
Get Latest ScoreTool to retrieve the latest nps score for the account.
Get OutboxRetrieve the outbox of surveys that are scheduled to be sent.
Get ReportsTool to retrieve reports related to nps surveys, including scores and trends.
Get TemplatesTool to retrieve a list of survey templates available in the account.
Send Transactional SurveyTool to send a transactional survey to customers.
Unsubscribe CustomersUnsubscribe customers from receiving surveys by providing their email addresses.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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 Retently 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 python-dotenv
What's happening:
  • composio connects your agent to Retently via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools 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
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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 Retently MCP URL

Create a Composio Tool Router session for Retently

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["retently"],
)
url = session.mcp.url
What's happening:
  • You create a Retently only session through Composio
  • Composio returns an MCP HTTP URL that exposes Retently tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Retently Assistant",
    goal="Help users interact with Retently through natural language commands",
    backstory=(
        "You are an expert assistant with access to Retently tools. "
        "You can perform various Retently operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Retently MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Retently operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Retently related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_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:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_retently_agent.py

Complete Code

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

python
# file: crewai_retently_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Retently session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["retently"],
    )
    url = session.mcp.url

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

    # Create Retently assistant agent
    toolkit_agent = Agent(
        role="Retently Assistant",
        goal="Help users interact with Retently through natural language commands",
        backstory=(
            "You are an expert assistant with access to Retently tools. "
            "You can perform various Retently operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Retently operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Retently related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

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

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Retently through Composio's Tool Router. The agent can perform Retently 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

How to build Retently MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Retently MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Retently MCP with Claude Agent SDK

Google ADK

Google ADK

Use Retently MCP with Google ADK

LangChain

LangChain

Use Retently MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Retently MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Retently MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Retently MCP with LlamaIndex

FAQ

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

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

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

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

Used by agents from

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Context
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