How to integrate Check Cherry MCP with Autogen

This guide walks you through connecting Check Cherry to AutoGen 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 AutoGen 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.

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Check Cherry

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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.

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Introduction

This guide will help you understand how to give your AutoGen 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

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

Get and set up your OpenAI and Composio API keys
Install the required dependencies for Autogen and Composio
Initialize Composio and create a Tool Router session for Check Cherry
Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
Configure an Autogen AssistantAgent that can call Check Cherry tools
Run a live chat loop where you ask the agent to perform Check Cherry operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

Multi-Agent Systems: Build collaborative agent workflows
MCP Workbench: Native support for Model Context Protocol tools
Streaming HTTP: Connect to external services through streamable HTTP
AssistantAgent: Pre-built agent class for tool-using assistants

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:

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

Step-by-step Guide

Step by step08 STEPS

Prerequisites

You will need:

A Composio API key
An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
A Check Cherry account you can connect to Composio
Some basic familiarity with Autogen and Python async

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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

composio connects your agent to Check Cherry via MCP
autogen-agentchat provides the AssistantAgent class
autogen-ext-openai provides the OpenAI model client
autogen-ext-tools provides MCP workbench support

Set up environment variables

bash

COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

COMPOSIO_API_KEY is required to talk to Composio
OPENAI_API_KEY is used by Autogen's OpenAI client
USER_ID is how Composio identifies which user's Check Cherry connections to use

Import dependencies and create Tool Router session

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

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

What's happening:

load_dotenv() reads your .env file
Composio(api_key=...) initializes the SDK
create(...) creates a Tool Router session that exposes Check Cherry tools
session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python

# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

url points to the Tool Router MCP endpoint from Composio
timeout is the HTTP timeout for requests
sse_read_timeout controls how long to wait when streaming responses
terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python

# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Check Cherry assistant agent with MCP tools
    agent = AssistantAgent(
        name="check_cherry_assistant",
        description="An AI assistant that helps with Check Cherry operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

OpenAIChatCompletionClient wraps the OpenAI model for Autogen
McpWorkbench connects the agent to the MCP tools
AssistantAgent is configured with the Check Cherry tools from the workbench

Run the interactive chat loop

python

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Check Cherry related question or task to the agent.\n")

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

What's happening:

The script prompts you in a loop with You:
Autogen passes your input to the model, which decides which Check Cherry tools to call via MCP
agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Check Cherry and AutoGen:

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

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

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Check Cherry assistant agent with MCP tools
        agent = AssistantAgent(
            name="check_cherry_assistant",
            description="An AI assistant that helps with Check Cherry operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Check Cherry related question or task to the agent.\n")

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Check Cherry through Composio's Tool Router and MCP. From here you can:

Add more toolkits to the toolkits list, for example notion or hubspot
Refine the agent description to point it at specific workflows
Wrap this script behind a UI, Slack bot, or internal tool

Once the pattern is clear for Check Cherry, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

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.

FRAMEWORKS

How to build Check Cherry MCP Agent with another framework

OpenAI Agents SDK

Use Check Cherry MCP with OpenAI Agents SDK

Claude Agent SDK

Use Check Cherry MCP with Claude Agent SDK

Claude Code

Use Check Cherry MCP with Claude Code

Claude Cowork

Use Check Cherry MCP with Claude Cowork

Codex

Use Check Cherry MCP with Codex

OpenClaw

Use Check Cherry MCP with OpenClaw

Hermes

Use Check Cherry MCP with Hermes

CLI

Use Check Cherry MCP with CLI

Google ADK

Use Check Cherry MCP with Google ADK

LangChain

Use Check Cherry MCP with LangChain

Vercel AI SDK

Use Check Cherry MCP with Vercel AI SDK

Mastra AI

Use Check Cherry MCP with Mastra AI

LlamaIndex

Use Check Cherry MCP with LlamaIndex

CrewAI

Use Check Cherry MCP with CrewAI

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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. Autogen 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.

Start with Check Cherry.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Check Cherry tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Check Cherry MCP with Autogen

Connect Check Cherry without auth hassles

Introduction

Also integrate Check Cherry with

TL;DR

What is AutoGen?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

Supported Tools

How to build Check Cherry MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Amazing Marvin

ApptiveGrid

Bookingmood

Booqable

Frequently asked questions

What are the differences in Tool Router MCP and Check Cherry MCP?+

Can I use Tool Router MCP with Autogen?+

Can I manage the permissions and scopes for Check Cherry while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Check Cherry.It takes 30 seconds.

Teams ship real agents on Composio