How to integrate Check Cherry MCP with CrewAI

This guide walks you through connecting Check Cherry to CrewAI using the Composio tool router. By the end, you'll have a working Check Cherry agent that can list your appointments, check event availability, create appointments, track expenses, and view reports through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Check Cherry account through Composio's Check Cherry MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Check Cherry logoCheck Cherry
Api Key

Check Cherry is a business management platform for photography and event service companies. It helps teams manage leads, bookings, appointments, offerings, payments, and workflows in one place.

25 Tools

Introduction

This guide walks you through connecting Check Cherry to CrewAI using the Composio tool router. By the end, you'll have a working Check Cherry agent that can list your appointments, check event availability, create appointments, track expenses, and view reports through natural language commands.

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

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

Also integrate Check Cherry with

TL;DR

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

The Check Cherry MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Check Cherry account. It provides structured and secure access to your leads, bookings, appointments, offerings, payments, expenses, questionnaires, and business reports, so your agent can manage schedules, review clients and events, track finances, update questionnaires, and check availability on your behalf.

  • Lead and booking management: Have your agent search leads, review proposals and confirmed bookings, and find package bookings by event date.
  • Appointments and availability: Let the agent check package availability, find open appointment times, schedule staff appointments for guests, and remove appointments from the active calendar.
  • Payments and expense tracking: Direct your agent to review event payments and refunds, record business expenses, update expense details, classify costs, or remove outdated expense records.
  • Questionnaire coordination: Instruct your agent to attach questionnaire templates to bookings, review questions and responses, update answers, and remove questionnaires when needed.
  • Offerings and business reporting: Have your agent review services, package groups, packages, and add-ons, then retrieve reports covering bookings, leads, payments, proposals, or projected revenue.

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

Step by step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Check Cherry connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

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.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Check Cherry via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

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
5

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 Check Cherry MCP URL
6

Create a Composio Tool Router session for Check Cherry

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

url = session.mcp.url
What's happening:
  • You create a Check Cherry only session through Composio
  • Composio returns an MCP HTTP URL that exposes Check Cherry tools
7

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.
8

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 Check Cherry 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=["check_cherry"],
)
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 Check Cherry through Composio's Tool Router. The agent can perform Check Cherry 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
TOOLS

Supported Tools

Every Check Cherry action and event your agent gets out of the box.

Attach Questionnaire

Copy a questionnaire template onto a proposal or confirmed booking.

Check Event Availability

Check whether a package can be booked on one date or across a date range.

Create Appointment

Create a staff appointment for a guest at a specific date, time, and duration; this changes the business calendar.

Create Expense

Record a business expense with its payment date, amount, reference, payee, and optional event and category links.

Delete Appointment

Soft-delete one appointment so it no longer appears in active business calendar records; Check Cherry retains the deleted record.

Delete Expense

Soft-delete one business expense so it no longer appears in listings; Check Cherry retains the record for record-keeping.

Get Appointment

Return full guest, scheduling, and assignment details for one appointment.

Get Event

Return full details for one proposal or confirmed booking by event ID.

Get Lead

Return full details for one lead by ID.

Get Questionnaire

Return one questionnaire with its questions, current answers, and completion state.

Get Report

Return one time-bucketed Check Cherry business report for bookings, leads, payments, proposals, or projected revenue.

List Appointments

Search staff appointments by date, assignee, brand, cancellation state, time direction, or text, returning one page.

List Appointment Slots

Return available appointment times for one staff member and appointment calendar on a selected date.

List Event Bookings

List package bookings attached to events within an optional event-date range, returning one page and a continuation cursor.

List Events

Search proposals and confirmed bookings with date, status, client, staff, payment, and workflow filters, returning one page.

List Expense Categories

Return the expense categories available for classifying business expenses.

List Expenses

Search business expenses by event, category, payment date, text, and sort order, returning one page.

List Leads

Search leads by exact email or contact and event text, returning one page of CRM records.

List Offerings

List one Check Cherry catalog resource: services, package groups, packages, or add-ons.

List Payments

List event payment records, including received payments and refunds, by event or payment date; returns one page with monetary values in Check Cherry's original representations.

List Questionnaires

List questionnaires attached to one event booking.

List Questionnaire Templates

Return questionnaire templates that can be attached to an event booking.

Remove Questionnaire

Detach one questionnaire from its event booking and delete its collected answers; the source questionnaire template is unchanged.

Update Expense

Change selected fields on an existing business expense.

Update Questionnaire Answers

Update answers on a standard questionnaire.

FAQ

Frequently asked questions

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

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 Check Cherry tools.

Yes, absolutely. You can configure which Check Cherry 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.

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 Check Cherry data and credentials are handled as safely as possible.

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