How to integrate Intercom MCP with LangChain

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Introduction

This guide walks you through connecting Intercom to LangChain using the Composio tool router. By the end, you'll have a working Intercom agent that can add tag 'vip' to contact john doe, assign open conversation #123 to support team, create note for contact emily about refund, close all resolved conversations from today through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Intercom project to Composio
  • Create a Tool Router MCP session for Intercom
  • Initialize an MCP client and retrieve Intercom tools
  • Build a LangChain agent that can interact with Intercom
  • 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 Intercom MCP server, and what's possible with it?

The Intercom MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Intercom account. It provides structured and secure access to your customer engagement platform, so your agent can perform actions like managing conversations, tagging contacts, creating articles, and updating company records on your behalf.

  • Conversation management and assignment: Let your agent assign conversations to teams or admins, create new conversations, and close them when resolved, streamlining your support workflow.
  • Contact tagging and note creation: Effortlessly tag contacts with relevant labels or add detailed notes for context, making customer follow-ups more organized and actionable.
  • Automated company and contact updates: Enable your agent to attach contacts to companies, create or update company records, and keep your Intercom data clean and up to date.
  • Article and collection creation: Let your agent publish new articles or create help center collections to expand your self-serve support resources without manual effort.
  • Subscription and message preferences management: Allow your agent to add or manage subscriptions for contacts, helping you personalize communication and respect user preferences automatically.

Supported Tools & Triggers

Tools
Add subscription to a contactYou can add a specific subscription to a contact.
Add tag to a contactYou can tag a specific contact.
Assign conversationAssigns a conversation to a specific admin or team in intercom
Attach a contact to a companyYou can attach a company to a single contact.
Close conversationCloses a conversation in intercom, marking it as resolved
Create a collectionYou can create a new collection by making a post request to `https://api.
Create an articleYou can create a new article by making a post request to `https://api.
Create a noteYou can add a note to a single contact.
Create conversationCreates a new conversation in intercom
Create or update a companyYou can create or update a company.
Delete a collectionYou can delete a single collection by making a delete request to `https://api.
Delete a companyYou can delete a single company.
Delete a contactYou can delete a single contact.
Delete an articleYou can delete a single article by making a delete request to `https://api.
Detach a contact from a companyYou can detach a company from a single contact.
Get a contactYou can fetch the details of a single contact.
Get conversationRetrieves a specific conversation by id with all messages and details
Identify an adminYou can view the currently authorised admin along with the embedded app object (a "workspace" in legacy terminology).
List all activity logsYou can get a log of activities by all admins in an app.
List all adminsYou can fetch a list of admins for a given workspace.
List all articlesYou can fetch a list of all articles by making a get request to `https://api.
List all collectionsYou can fetch a list of all collections by making a get request to `https://api.
List all companiesYou can list companies.
List all help centersYou can list all help centers by making a get request to `https://api.
List all notesYou can fetch a list of notes that are associated to a contact.
List attached companies for contactYou can fetch a list of companies that are associated to a contact.
List attached contactsYou can fetch a list of all contacts that belong to a company.
List attached segments for companiesYou can fetch a list of all segments that belong to a company.
List attached segments for contactYou can fetch a list of segments that are associated to a contact.
List conversationsLists conversations from intercom with filtering and pagination support
List subscriptions for a contactYou can fetch a list of subscription types that are attached to a contact.
List tags attached to a contactYou can fetch a list of all tags that are attached to a specific contact.
Merge a lead and a userYou can merge a contact with a `role` of `lead` into a contact with a `role` of `user`.
Remove subscription from a contactYou can remove a specific subscription from a contact.
Remove tag from a contactYou can remove tag from a specific contact.
Reopen conversationReopens a closed conversation in intercom
Reply to conversationSends a reply to an existing conversation in intercom
Retrieve a collectionYou can fetch the details of a single collection by making a get request to `https://api.
Retrieve a company by idYou can fetch a single company.
Retrieve a help centerYou can fetch the details of a single help center by making a get request to `https://api.
Retrieve an adminYou can retrieve the details of a single admin.
Retrieve an articleYou can fetch the details of a single article by making a get request to `https://api.
Retrieve companiesYou can fetch a single company by passing in `company id` or `name`.
Scroll over all companiesThe `list all companies` functionality does not work well for huge datasets, and can result in errors and performance problems when paging deeply.
Search conversationsSearches for conversations using query string with support for filtering and sorting
Search for articlesYou can search for articles by making a get request to `https://api.
Set an admin to awayYou can set an admin as away for the inbox.
Update a collectionYou can update the details of a single collection by making a put request to `https://api.
Update a companyYou can update a single company using the intercom provisioned `id`.
Update a contactYou can update an existing contact (ie.
Update an articleYou can update the details of a single article by making a put request to `https://api.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

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

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

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv loads environment variables

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

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

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

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
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 Intercom tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Intercom
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['intercom']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Intercom 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 Intercom tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "intercom-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

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

Set up interactive chat interface

conversation_history = []

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

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['intercom']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "intercom-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Intercom related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Intercom 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.

How to build Intercom MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Intercom MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Intercom MCP with Claude Agent SDK

Google ADK

Google ADK

Use Intercom MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Intercom MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Intercom MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Intercom MCP with LlamaIndex

CrewAI

CrewAI

Use Intercom MCP with CrewAI

Claude Code

Claude Code

Use Intercom MCP with Claude Code

FAQ

What are the differences in Tool Router MCP and Intercom MCP?

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

Can I use Tool Router MCP with LangChain?

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 Intercom tools.

Can I manage the permissions and scopes for Intercom while using Tool Router?

Yes, absolutely. You can configure which Intercom 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 Intercom data and credentials are handled as safely as possible.

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