# How to integrate Retently MCP with CrewAI ```json { "title": "How to integrate Retently MCP with CrewAI", "toolkit": "Retently", "toolkit_slug": "retently", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/retently/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/retently/framework/crew-ai.md", "updated_at": "2026-05-12T10:24:07.004Z" } ``` ## 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 your account 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. ## Also integrate Retently with - [OpenAI Agents SDK](https://composio.dev/toolkits/retently/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/retently/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/retently/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/retently/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/retently/framework/codex) - [OpenClaw](https://composio.dev/toolkits/retently/framework/openclaw) - [Hermes](https://composio.dev/toolkits/retently/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/retently/framework/cli) - [Google ADK](https://composio.dev/toolkits/retently/framework/google-adk) - [LangChain](https://composio.dev/toolkits/retently/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/retently/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/retently/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/retently/framework/llama-index) ## 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 | Tool slug | Name | Description | |---|---|---| | `RETENTLY_ADD_FEEDBACK_TAGS` | Add Feedback Tags | Add tags to feedback items by providing feedback IDs and corresponding tags. | | `RETENTLY_ADD_FEEDBACK_TOPICS` | Add Feedback Topics | Add topics to feedback items by providing feedback IDs and corresponding topics. | | `RETENTLY_CREATE_OR_UPDATE_CUSTOMERS` | Create or Update Customers | Tool to create new customers or update existing ones by providing their details, including email, name, company, tags, and properties. Use this to manage your customer base in Retently. | | `RETENTLY_DELETE_CUSTOMERS` | Delete Customers | Delete customers from the workspace by providing their unique IDs. | | `RETENTLY_GET_CAMPAIGNS` | Get Campaigns | Tool to retrieve a list of campaigns associated with the account. Use when you need to get details about all campaigns. | | `RETENTLY_GET_CUSTOMER_BY_ID` | Get Customer By ID | Tool to retrieve detailed information about a specific customer by their unique ID. Use when you need to get all the details of a customer. | | `RETENTLY_GET_CUSTOMERS` | Get Customers | Retrieve a list of customers with optional parameters for pagination, sorting, and filtering by email or date range. | | `RETENTLY_GET_FEEDBACK` | Get Feedback | Tool to retrieve feedback received from customers. Use when you need to get a list of feedback, with optional parameters for pagination and sorting. | | `RETENTLY_GET_FEEDBACK_BY_ID` | Get Feedback by ID | Tool to retrieve detailed information about specific feedback by its unique ID. Use when you need to get the details of a single feedback entry. | | `RETENTLY_GET_LATEST_SCORE` | Get Latest Score | Tool to retrieve the latest NPS score for the account. Use when you need to get the most up-to-date NPS score. | | `RETENTLY_GET_OUTBOX` | Get Outbox | Retrieve the outbox of surveys that are scheduled to be sent. | | `RETENTLY_GET_REPORTS` | Get Reports | Tool to retrieve reports related to NPS surveys, including scores and trends. Use when you need to get campaign performance data. | | `RETENTLY_GET_TEMPLATES` | Get Templates | Tool to retrieve a list of survey templates available in the account. Use when you need to get the available survey templates. | | `RETENTLY_SEND_TRANSACTIONAL_SURVEY` | Send Transactional Survey | Tool to send a transactional survey to customers. Use when you need to send a survey to a customer after a specific event, with an optional delay. | | `RETENTLY_UNSUBSCRIBE_CUSTOMERS` | Unsubscribe Customers | Unsubscribe customers from receiving surveys by providing their email addresses. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Retently MCP server is an implementation of the Model Context Protocol that connects your AI agent to Retently. It provides structured and secure access so your agent can perform Retently 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 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 ### 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 **What's happening:** - composio connects your agent to Retently via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables 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 ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **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 ```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") ``` ### 5. Create a Composio Tool Router session for Retently **What's happening:** - You create a Retently only session through Composio - Composio returns an MCP HTTP URL that exposes Retently tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["retently"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **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. ```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, ) ``` ### 7. Create a CLI Chatloop and define the Crew **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. ```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") ``` ## Complete Code ```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=["retently"], ) 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 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](https://composio.dev/toolkits/retently/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/retently/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/retently/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/retently/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/retently/framework/codex) - [OpenClaw](https://composio.dev/toolkits/retently/framework/openclaw) - [Hermes](https://composio.dev/toolkits/retently/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/retently/framework/cli) - [Google ADK](https://composio.dev/toolkits/retently/framework/google-adk) - [LangChain](https://composio.dev/toolkits/retently/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/retently/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/retently/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/retently/framework/llama-index) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data. - [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly. - [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools. - [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping. - [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights. - [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space. - [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently. - [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment. - [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries. - [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business. - [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and 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. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)