How to integrate Zendesk MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Zendesk to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Zendesk agent that can create a new zendesk ticket for a customer, list all open tickets assigned to me, summarize this week's new zendesk tickets, get details for ticket #12345 through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Zendesk account through Composio's Zendesk 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
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Zendesk
  • Configure an AI agent that can use Zendesk as a tool
  • Run a live chat session where you can ask the agent to perform Zendesk operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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

The Zendesk MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zendesk account. It provides structured and secure access to your helpdesk, so your agent can create tickets, retrieve ticket details, manage organizations, and streamline customer support workflows on your behalf.

  • Automated ticket creation and management: Let your agent create new support tickets, update details, or delete tickets to keep your helpdesk organized and responsive.
  • Advanced ticket search and retrieval: Quickly pull up lists of open, pending, or filtered tickets, or fetch specific ticket details for efficient support resolution.
  • Organization management: Effortlessly create, retrieve, or delete organizations in your Zendesk account to keep customer data up to date.
  • Account insights and user info: Have your agent fetch account owner information or organization details for better context in customer interactions.
  • Scalable support automation: Use AI-powered workflows to triage, route, and manage high volumes of tickets without manual intervention.

Supported Tools & Triggers

Tools
Triggers
Count Zendesk OrganizationsCount the number of organizations in zendesk.
Create Zendesk OrganizationCreate an organization in zendesk.
Create Zendesk TicketCreate a ticket in zendesk.
Delete Zendesk OrganizationDelete an organization in zendesk.
Delete Zendesk TicketDelete a ticket in zendesk.
Get Zendesk About MeGet information about the owner account in zendesk.
Get Zendesk OrganizationsGet all organizations in zendesk.
Get Zendesk OrganizationGet information about a specific organization in zendesk.
Get Zendesk TicketGet ticket details from zendesk.
List TicketsList zendesk tickets with pagination and filtering.
Reply to Zendesk TicketAction to reply to a zendesk ticket by adding a comment.
Update Zendesk OrganizationUpdate an organization in zendesk.

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:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Zendesk project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Zendesk.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Zendesk Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["zendesk"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only zendesk.
  • The router checks the user's Zendesk connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Zendesk.
  • This approach keeps things lightweight and lets the agent request Zendesk tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Zendesk. "
        "Help users perform Zendesk operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Zendesk and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Zendesk operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Zendesk.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Zendesk and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["zendesk"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Zendesk. "
        "Help users perform Zendesk operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Zendesk MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Zendesk.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

How to build Zendesk MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

Use Zendesk MCP with Claude Agent SDK

Google ADK

Google ADK

Use Zendesk MCP with Google ADK

LangChain

LangChain

Use Zendesk MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Zendesk MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Zendesk MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Zendesk MCP with LlamaIndex

CrewAI

CrewAI

Use Zendesk MCP with CrewAI

FAQ

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

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

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 Zendesk tools.

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

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

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