How to integrate Classmarker MCP with Autogen

This guide walks you through connecting Classmarker to AutoGen using the Composio tool router. By the end, you'll have a working Classmarker agent that can add student to biology exam access list, create a new question for math quiz, delete user account for withdrawn student through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Classmarker account through Composio's Classmarker MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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ClassMarker is a professional online quiz maker for business and education. It provides instant grading, flexible test design, and in-depth reporting.

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Introduction

This guide walks you through connecting Classmarker to AutoGen using the Composio tool router. By the end, you'll have a working Classmarker agent that can add student to biology exam access list, create a new question for math quiz, delete user account for withdrawn student through natural language commands.

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

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

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

The Classmarker MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Classmarker account. It provides structured and secure access to your quiz management tools, so your agent can create tests, manage users and groups, add questions, and control access codes—without manual intervention.

  • Automated user and group management: Let your agent create new users, add them to groups, or delete users and groups for streamlined participant organization.
  • Dynamic question and category creation: Instruct your agent to add new questions or categories to your exams, helping you build tests faster and keep content organized.
  • Access code and permissions control: Enable your agent to generate, assign, or delete access codes for specific exams, giving or revoking test access instantly as needed.
  • Test link and API key management: Allow your agent to manage test links or revoke API keys to maintain secure and up-to-date exam distribution.
  • Efficient data cleanup: Ask your agent to remove users, groups, test links, or access codes, keeping your Classmarker account tidy and up to date with minimal effort.

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

Supported Tools

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

Create Access List Item

Tool to add one or more access codes to an access list.

Create a new question category

Tool to create a new question category.

Create Group

Tool to create a new group.

Create Question

Tool to create a new question with specified text, type, and category in ClassMarker.

Create ClassMarker User

Tool to create a new user in ClassMarker.

Delete Access List Item

Removes one or more access codes from a ClassMarker access list.

Delete API Key

Tool to delete an API key by its ID.

Delete Group

Tool to delete a group by its ID.

Delete Test Link

Deletes a specific link assignment from a test/exam in ClassMarker.

Delete User

Tool to delete a specific user by ID.

Delete Webhook

Deletes a webhook listener from your ClassMarker account.

Get Group Details

Retrieve detailed information about a specific group by ID.

Get Initial Finished After Timestamp

Compute the initial finishedAfterTimestamp for paginating ClassMarker test results.

Get Question

Tool to retrieve a specific question by its ID.

Get Recent Results For Group Exam

Tool to fetch recent test results for a specific group and exam.

Get Recent Results Link Exam

Fetch recent exam results for a specific link and test combination.

Get Test Details

Retrieves detailed information for a specific test (exam) including its name and all assignment contexts.

Get User Details

Tool to retrieve detailed information about a specific user.

List Question Categories

Retrieves all question categories organized in a hierarchical structure.

List Certificates

Tool to list all certificates.

List Questions

Retrieve a paginated list of questions from your question bank.

List Recent Results For Groups

Retrieve recent exam results for all groups in your ClassMarker account.

List Recent Results for Links

Retrieves recent test results from ALL links accessible to your API key.

List Users

Tool to list all users.

List Webhooks

Tool to retrieve all configured webhooks.

Update Sub-Category

Tool to update an existing sub-category.

Update an existing parent category

Tool to update an existing parent category.

Update Question

Updates an existing question in the ClassMarker question bank.

FRAMEWORKS

How to build Classmarker MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Classmarker MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Classmarker MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Classmarker MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Classmarker MCP with Claude Cowork

Codex logo

Codex

Use Classmarker MCP with Codex

OpenClaw logo

OpenClaw

Use Classmarker MCP with OpenClaw

Hermes logo

Hermes

Use Classmarker MCP with Hermes

CLI logo

CLI

Use Classmarker MCP with CLI

Google ADK logo

Google ADK

Use Classmarker MCP with Google ADK

LangChain logo

LangChain

Use Classmarker MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Classmarker MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Classmarker MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Classmarker MCP with LlamaIndex

CrewAI logo

CrewAI

Use Classmarker MCP with CrewAI

FAQ

Frequently asked questions

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

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

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