How to integrate Onedesk MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Onedesk to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Onedesk agent that can log two hours on today's support ticket, remove outdated attachment from project alpha, delete task 'update onboarding guide' from project, delete ticket resolved last friday through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Onedesk account through Composio's Onedesk 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 Onedesk
  • Configure an AI agent that can use Onedesk as a tool
  • Run a live chat session where you can ask the agent to perform Onedesk 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 Onedesk MCP server, and what's possible with it?

The Onedesk MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Onedesk account. It provides structured and secure access to your help desk and project management workspace, so your agent can perform actions like managing tickets, handling tasks, logging work, and cleaning up projects on your behalf.

  • Automated worklog entry creation: Let your agent log time spent on tickets, tasks, or projects, so you can track team effort without manual entry.
  • Ticket and task cleanup: Direct your agent to delete tickets or tasks that are no longer needed, keeping your workspace organized and up to date.
  • Project and requirement management: Have the agent remove outdated projects or requirements, ensuring your portfolio stays relevant and clutter-free.
  • Attachment and comment removal: Ask your agent to delete attachments or comments from tasks, tickets, or projects, maintaining a clean and focused workflow.
  • Customer and timesheet handling: Enable your agent to securely delete customers or timesheets, helping you maintain accurate records and compliance.

Supported Tools & Triggers

Tools
Create Worklog EntryTool to create a worklog entry.
Delete AttachmentTool to delete a specific attachment.
Delete CommentTool to delete a specific comment by its id.
Delete CustomerTool to delete a customer.
Delete MessageTool to delete a specific message.
Delete ProjectTool to delete a project by its id.
Delete RequirementTool to delete a requirement.
Delete TaskTool to delete a specific task.
Delete TicketTool to delete a ticket by its id.
Delete TimesheetTool to delete a timesheet by its id.
Delete UserTool to delete a user by its id.
Delete WorklogTool to delete a worklog by its id.
Get AttachmentsTool to retrieve a list of attachments.
Get Comment DetailsTool to retrieve detailed information for a comment by its id.
Get CommentsTool to retrieve all comments.
Get Feedback ItemsTool to retrieve all feedback items.
Get Feedback DetailsTool to retrieve details of a specific feedback item.
Get Issue DetailsTool to retrieve details of a specific issue.
Get IssuesTool to retrieve a list of issues.
Get MessagesTool to retrieve a list of messages.
Get Requirement DetailsTool to retrieve full details of a specific requirement.
Get RequirementsTool to retrieve a list of requirements.
Get Task DetailsTool to retrieve details of a specific task in onedesk.
Get TicketsTool to retrieve a list of tickets.
Get Timesheet DetailsTool to retrieve details of a specific timesheet entry.
Get TimesheetsTool to retrieve a list of timesheet entries.
Get Worklog DetailsTool to retrieve details of a specific worklog.
Get WorklogsTool to retrieve all worklogs.

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 Onedesk 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 Onedesk.

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 Onedesk Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["onedesk"]
)

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 onedesk.
  • The router checks the user's Onedesk connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Onedesk.
  • This approach keeps things lightweight and lets the agent request Onedesk 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 Onedesk. "
        "Help users perform Onedesk 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 Onedesk 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 Onedesk 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 Onedesk.
  • 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 Onedesk 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=["onedesk"]
)
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 Onedesk. "
        "Help users perform Onedesk 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 Onedesk MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Onedesk.

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 Onedesk MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

Use Onedesk MCP with Claude Agent SDK

Google ADK

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Use Onedesk MCP with Google ADK

LangChain

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Use Onedesk MCP with LangChain

Vercel AI SDK

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Use Onedesk MCP with Vercel AI SDK

Mastra AI

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Use Onedesk MCP with Mastra AI

LlamaIndex

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Use Onedesk MCP with LlamaIndex

CrewAI

CrewAI

Use Onedesk MCP with CrewAI

FAQ

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

With a standalone Onedesk MCP server, the agents and LLMs can only access a fixed set of Onedesk tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Onedesk 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 Onedesk tools.

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

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

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