How to integrate Zoho MCP with Autogen

This guide walks you through connecting Zoho to AutoGen using the Composio tool router. By the end, you'll have a working Zoho agent that can convert new leads to contacts in crm, add a note to an existing zoho deal, list all contacts added this week through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Zoho account through Composio's Zoho MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Zoho is a suite of cloud business apps for CRM, email marketing, and collaboration. It helps teams automate workflows and scale operations effortlessly.

14 Tools

Introduction

This guide walks you through connecting Zoho to AutoGen using the Composio tool router. By the end, you'll have a working Zoho agent that can convert new leads to contacts in crm, add a note to an existing zoho deal, list all contacts added this week through natural language commands.

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

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

Also integrate Zoho with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Zoho
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Zoho tools
  • Run a live chat loop where you ask the agent to perform Zoho operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

What is the Zoho MCP server, and what's possible with it?

The Zoho MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zoho account. It provides structured and secure access to your Zoho CRM data, so your agent can perform actions like creating leads, updating records, tagging entries, and managing relationships between your contacts and accounts automatically.

  • Automated lead conversion: Instantly convert Zoho CRM leads into contacts, accounts, and even deals without manual effort.
  • Effortless CRM record creation: Direct your agent to add new records to any Zoho CRM module—like leads, contacts, or deals—in seconds.
  • Bulk record retrieval and updates: Let your agent fetch lists of records or update details across multiple modules to keep your CRM up to date.
  • Tag and organize CRM data: Have your agent create and apply new tags for streamlined segmentation and easier tracking of customers or activities.
  • Manage cross-module relationships: Enable your agent to associate or update related records between contacts, accounts, and other CRM modules for richer data connections.

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Zoho account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Zoho via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Zoho connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Zoho session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["zoho"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Zoho tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Zoho assistant agent with MCP tools
    agent = AssistantAgent(
        name="zoho_assistant",
        description="An AI assistant that helps with Zoho operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Zoho tools from the workbench
8

Run the interactive chat loop

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

# Conversation loop
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")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Zoho tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Zoho and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Zoho session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["zoho"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Zoho assistant agent with MCP tools
        agent = AssistantAgent(
            name="zoho_assistant",
            description="An AI assistant that helps with Zoho operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

        # Conversation loop
        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")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

Conclusion

You now have an Autogen assistant wired into Zoho through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Zoho, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Zoho action and event your agent gets out of the box.

Convert Zoho CRM Lead

Converts a lead into a contact, account, and optionally a deal in Zoho CRM.

Create Zoho CRM Record

Creates new records in a specified module in Zoho CRM.

Create Zoho CRM Tag

Creates a new tag in Zoho CRM for a specific module.

Get Zoho CRM Module Fields Metadata

Retrieves field metadata for a Zoho CRM module including API names, data types, permissions, and configuration details.

Get Zoho CRM Related Lists Metadata

Retrieves related list metadata for a Zoho CRM module to discover correct api_name values.

Get Zoho CRM Related Records

Fetch related-list records (e.

Get Zoho CRM Records

Retrieves records from a specified module in Zoho CRM.

Get Zoho CRM Users

Tool to retrieve users from Zoho CRM.

List Zoho CRM Modules

Lists all available Zoho CRM modules (standard + custom) to reliably select module API names/IDs for operations.

List Attachments for Zoho CRM Record

Tool to list attachment metadata (id, File_Name, Size, Created_Time, etc.

Search Zoho CRM Records

Search for records within a Zoho CRM module using server-side queries.

Update Related Records in Zoho CRM

Associates or updates relationships between records across different modules in Zoho CRM.

Update Zoho CRM Record

Updates existing records in a specified module in Zoho CRM.

Upload Attachment to Zoho CRM Record

Tool to upload a file or attach a URL as an Attachment to a specific Zoho CRM record.

FRAMEWORKS

How to build Zoho MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Zoho MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Zoho MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Zoho MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Zoho MCP with Claude Cowork

Codex logo

Codex

Use Zoho MCP with Codex

OpenClaw logo

OpenClaw

Use Zoho MCP with OpenClaw

Hermes logo

Hermes

Use Zoho MCP with Hermes

CLI logo

CLI

Use Zoho MCP with CLI

Google ADK logo

Google ADK

Use Zoho MCP with Google ADK

LangChain logo

LangChain

Use Zoho MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Zoho MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Zoho MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Zoho MCP with LlamaIndex

CrewAI logo

CrewAI

Use Zoho MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Autogen 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 Zoho tools.

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

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