How to integrate Onedesk MCP with Autogen

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Introduction

This guide walks you through connecting Onedesk to AutoGen 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 By the end, you'll have a working Onedesk agent that can log two hours on today's support ticket, remove outdated attachment from project alpha, delete task 'update onboarding guide' from project, delete ticket resolved last friday through natural language commands.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 AutoGen 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 required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Onedesk
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Onedesk tools
  • Run a live chat loop where you ask the agent to perform Onedesk 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 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  • Project and requirement management: Have the agent remove outdated projects or requirements, ensuring your portfolio stays relevant and clutter-free.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

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Onedesk account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

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 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 Onedesk via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

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 Onedesk connections to use

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 Onedesk session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["onedesk"]
    )
    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 Onedesk tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

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

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 Onedesk assistant agent with MCP tools
    agent = AssistantAgent(
        name="onedesk_assistant",
        description="An AI assistant that helps with Onedesk 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 Onedesk tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Onedesk 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 Onedesk 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 Onedesk 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 Onedesk session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["onedesk"]
    )
    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 Onedesk assistant agent with MCP tools
        agent = AssistantAgent(
            name="onedesk_assistant",
            description="An AI assistant that helps with Onedesk 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 Onedesk 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 Onedesk 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 Onedesk, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Onedesk MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Onedesk MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Onedesk MCP with Claude Agent SDK

Google ADK

Google ADK

Use Onedesk MCP with Google ADK

LangChain

LangChain

Use Onedesk MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Onedesk MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Onedesk MCP with Mastra AI

LlamaIndex

LlamaIndex

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 Autogen?

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 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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