How to integrate Request Tracker (RT) MCP with Autogen

This guide walks you through connecting Request Tracker (RT) to AutoGen using the Composio tool router. By the end, you'll have a working Request Tracker (RT) agent that can search your support tickets, create tickets, review ticket history, reply to tickets, and update tickets through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Request Tracker (RT) account through Composio's Request Tracker (RT) MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Request Tracker (RT) is a ticketing and workflow platform for managing support requests, queues, users, assets, and knowledge articles. It helps support and operations teams track work, enforce process, and keep request history in one place.

11 Tools

Introduction

This guide walks you through connecting Request Tracker (RT) to AutoGen using the Composio tool router. By the end, you'll have a working Request Tracker (RT) agent that can search your support tickets, create tickets, review ticket history, reply to tickets, and update tickets through natural language commands.

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

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

Also integrate Request Tracker (RT) 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 Request Tracker (RT)
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Request Tracker (RT) tools
  • Run a live chat loop where you ask the agent to perform Request Tracker (RT) 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 Request Tracker (RT) MCP server, and what's possible with it?

The Request Tracker (RT) MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Request Tracker (RT) account. It provides structured and secure access to your tickets, queues, users, ticket histories, and attachment details, so your agent can search, create, review, update, and respond to tickets on your behalf.

  • Ticket search and review: Have your agent find tickets using specific criteria and retrieve their current details for quick review.
  • Ticket creation and updates: Direct your agent to create tickets in the right queue or update selected ticket details as work progresses.
  • Replies and internal comments: Let the agent send public replies to requesters or add private comments for your support team.
  • Queue and user lookup: Instruct your agent to browse available queues and find users when choosing ticket requesters or owners.
  • History and attachment review: Have your agent review ticket activity, field changes, comments, timestamps, and available attachment details.

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 Request Tracker (RT) 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 Request Tracker (RT) 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 Request Tracker (RT) 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 Request Tracker (RT) session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["request_tracker"]
    )
    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 Request Tracker (RT) 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 Request Tracker (RT) assistant agent with MCP tools
    agent = AssistantAgent(
        name="request_tracker_assistant",
        description="An AI assistant that helps with Request Tracker (RT) 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 Request Tracker (RT) 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 Request Tracker (RT) 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 Request Tracker (RT) 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 Request Tracker (RT) 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 Request Tracker (RT) session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["request_tracker"]
    )
    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 Request Tracker (RT) assistant agent with MCP tools
        agent = AssistantAgent(
            name="request_tracker_assistant",
            description="An AI assistant that helps with Request Tracker (RT) 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 Request Tracker (RT) 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 Request Tracker (RT) 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 Request Tracker (RT), you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Request Tracker (RT) action and event your agent gets out of the box.

Add Ticket Comment

Add a private internal comment to an RT ticket.

Create Ticket

Create a durable RT ticket in a queue.

Get Queue

Retrieve one visible RT queue by id or exact name, including lifecycle and capability-sensitive links.

Get Ticket

Retrieve one visible RT ticket, including its current fields and capability-sensitive links for allowed follow-up operations.

Get Ticket History

Return one page of detailed ticket history, including transaction types, field changes, comments, creators, and timestamps.

List Queues

List queues visible to the connected RT user so an agent can select a queue for ticket search or creation.

List Ticket Attachments

List attachment metadata references associated with a visible RT ticket without downloading attachment content.

Reply to Ticket

Send public correspondence on an RT ticket.

Search Tickets

Search visible RT tickets with an explicit TicketSQL expression and return one bounded page.

Search Users

Search RT users with structured field conditions and return one bounded page of user references or selected fields for requester and owner lookup.

Update Ticket

Update selected metadata on an existing RT ticket and verify every requested field by reading it back.

FRAMEWORKS

How to build Request Tracker (RT) MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Request Tracker (RT) MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Request Tracker (RT) MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Request Tracker (RT) MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Request Tracker (RT) MCP with Claude Cowork

Codex logo

Codex

Use Request Tracker (RT) MCP with Codex

OpenClaw logo

OpenClaw

Use Request Tracker (RT) MCP with OpenClaw

Hermes logo

Hermes

Use Request Tracker (RT) MCP with Hermes

CLI logo

CLI

Use Request Tracker (RT) MCP with CLI

Google ADK logo

Google ADK

Use Request Tracker (RT) MCP with Google ADK

LangChain logo

LangChain

Use Request Tracker (RT) MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Request Tracker (RT) MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Request Tracker (RT) MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Request Tracker (RT) MCP with LlamaIndex

CrewAI logo

CrewAI

Use Request Tracker (RT) MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, absolutely. You can configure which Request Tracker (RT) 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 Request Tracker (RT) data and credentials are handled as safely as possible.

Start with Request Tracker (RT).It takes 30 seconds.

Managed auth, hosted MCP servers, and every Request Tracker (RT) tool your agent needs.Free to start.

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