How to integrate Check Cherry MCP with LangChain

This guide walks you through connecting Check Cherry to LangChain 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 LangChain 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 LangChain 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 LangChain 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:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Check Cherry project to Composio
  • Create a Tool Router MCP session for Check Cherry
  • Initialize an MCP client and retrieve Check Cherry tools
  • Build a LangChain agent that can interact with Check Cherry
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI 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

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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 your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Check Cherry functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Check Cherry tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['check_cherry']
    }
);

const url = session.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
  • This approach allows the agent to dynamically load and use Check Cherry tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "check_cherry-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Check Cherry MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Check Cherry tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

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

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

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

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['check_cherry']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "check_cherry-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Check Cherry related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Check Cherry through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
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

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. LangChain 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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