How to integrate Intercom MCP with Pydantic AI

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Introduction

This guide walks you through connecting Intercom to Pydantic AI 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 Pydantic AI 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:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Intercom
  • 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 Intercom 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 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, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active 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

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

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

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

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 Intercom
  • MCPServerStreamableHTTP connects to the Intercom MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

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 Intercom
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["intercom"],
    )
    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 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

Initialize the Pydantic AI Agent

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

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 Intercom.\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
  • Intercom API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

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 Intercom 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 Intercom
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["intercom"],
    )
    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
    intercom_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[intercom_mcp],
        instructions=(
            "You are a Intercom assistant. Use Intercom 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 Intercom.\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 Intercom through Composio's Tool Router. With this setup, your agent can perform real Intercom 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 + Intercom for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

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

LangChain

LangChain

Use Intercom MCP with LangChain

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

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 Pydantic AI?

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 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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