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How to integrate 2chat MCP with Pydantic AI

Connect Pydantic AI to 2chat MCP. Add new whatsapp contact for sales lead, list all whatsapp contacts and their details, and more using natural language, with authentication handled for you.

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Api Key

2chat is an API platform for WhatsApp and multichannel text messaging. It streamlines chat automation, group management, and real-time messaging for developers.

5 Tools

Introduction

This guide walks you through connecting 2chat to Pydantic AI using the Composio tool router. By the end, you'll have a working 2chat agent that can create and list contacts, check your API usage, review webhook subscriptions, and test your API key through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a 2chat account through Composio's 2chat MCP server.

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate 2chat with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAIAutoGen

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 2chat
  • 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 2chat 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 2chat MCP server, and what's possible with it?

The 2chat MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your 2chat account. It provides structured and secure access to your contacts, webhook subscriptions, API usage, and account information, so your agent can create contacts, list contacts, review webhook subscriptions, monitor API usage, and validate your API key on your behalf.

  • Contact creation: Have your agent add new contacts to your 2chat account.
  • Contact lookup: Let the agent list your existing 2chat contacts whenever you need them.
  • Webhook subscription review: Direct your agent to list configured webhook subscriptions for WhatsApp and phone call events.
  • API usage monitoring: Instruct your agent to retrieve your current API usage and account information.
  • API key validation: Have your agent test your 2chat API key and retrieve the related account information.

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 2chat
  • 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 2chat
  • MCPServerStreamableHTTP connects to the 2chat 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 2chat
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["_2chat"],
    )
    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 2chat 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
_2chat_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[_2chat_mcp],
    instructions=(
        "You are a 2chat assistant. Use 2chat tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the 2chat endpoint
  • The agent uses GPT-5 to interpret user commands and perform 2chat 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 2chat.\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
  • 2chat 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 2chat 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 2chat
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["_2chat"],
    )
    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
    _2chat_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[_2chat_mcp],
        instructions=(
            "You are a 2chat assistant. Use 2chat 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 2chat.\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 2chat through Composio's Tool Router. With this setup, your agent can perform real 2chat 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 + 2chat 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 2chat action and event your agent gets out of the box.

Create Contact

Tool to create a new contact in your 2Chat account.

Get API Usage Info

Tool to retrieve current API usage and account information.

List Contacts

Tool to list all contacts in your 2Chat account.

List Webhook Subscriptions

List all configured webhook subscriptions for WhatsApp and phone call events.

Test API Key

Tool to validate your API key and retrieve account info.

FRAMEWORKS

How to build 2chat MCP Agent with another framework

Claude Code logo

Claude Code

Use 2chat MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use 2chat MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use 2chat MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use 2chat MCP with Claude Agent SDK

Codex logo

Codex

Use 2chat MCP with Codex

OpenClaw logo

OpenClaw

Use 2chat MCP with OpenClaw

Hermes logo

Hermes

Use 2chat MCP with Hermes

Atomic Agent logo

Atomic Agent

Use 2chat MCP with Atomic Agent

Google ADK logo

Google ADK

Use 2chat MCP with Google ADK

LangChain logo

LangChain

Use 2chat MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use 2chat MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use 2chat MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use 2chat MCP with LlamaIndex

CrewAI logo

CrewAI

Use 2chat MCP with CrewAI

AutoGen logo

AutoGen

Use 2chat MCP with AutoGen

FAQ

Frequently asked questions

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