How to integrate Check Cherry MCP with Pydantic AI

This guide walks you through connecting Check Cherry to Pydantic AI 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 Pydantic AI 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 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 Pydantic AI 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 Pydantic AI 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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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Check Cherry
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Check Cherry workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Check Cherry
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
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

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Check Cherry
  • MCPServerStreamableHTTP connects to the Check Cherry MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Check Cherry
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["check_cherry"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Check Cherry tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
check_cherry_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[check_cherry_mcp],
    instructions=(
        "You are a Check Cherry assistant. Use Check Cherry tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Check Cherry endpoint
  • The agent uses GPT-5 to interpret user commands and perform Check Cherry operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Check Cherry.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Check Cherry API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Check Cherry
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["check_cherry"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    check_cherry_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[check_cherry_mcp],
        instructions=(
            "You are a Check Cherry assistant. Use Check Cherry tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Check Cherry.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Check Cherry through Composio's Tool Router. With this setup, your agent can perform real Check Cherry actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Check Cherry for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
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 logo

OpenAI Agents SDK

Use Check Cherry MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Check Cherry MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Check Cherry MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Check Cherry MCP with Claude Cowork

Codex logo

Codex

Use Check Cherry MCP with Codex

OpenClaw logo

OpenClaw

Use Check Cherry MCP with OpenClaw

Hermes logo

Hermes

Use Check Cherry MCP with Hermes

CLI logo

CLI

Use Check Cherry MCP with CLI

Google ADK logo

Google ADK

Use Check Cherry MCP with Google ADK

LangChain logo

LangChain

Use Check Cherry MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Check Cherry MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Check Cherry MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Check Cherry MCP with LlamaIndex

CrewAI logo

CrewAI

Use Check Cherry MCP with CrewAI

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. Pydantic AI 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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