How to integrate Classmarker MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Classmarker to the OpenAI Agents SDK 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agent that can add student to biology exam access list, create a new question for math quiz, delete user account for withdrawn student, organize new hires into onboarding group through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK 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.

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 Classmarker
  • Configure an AI agent that can use Classmarker as a tool
  • Run a live chat session where you can ask the agent to perform Classmarker 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 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.

Supported Tools & Triggers

Tools
Create Access List ItemTool to add one or more access codes to an access list.
Create a new question categoryTool to create a new question category.
Create GroupTool to create a new group.
Create QuestionTool to create a new question with specified text, type, and category.
Create ClassMarker UserTool to create a new user in ClassMarker.
Delete Access List ItemTool to delete one or more codes from an access list.
Delete API KeyTool to delete an API key by its ID.
Delete GroupTool to delete a group by its ID.
Delete Test LinkTool to delete a specific test link.
Delete UserTool to delete a specific user by ID.
Delete WebhookTool to delete a specific webhook listener.
Get All Groups, Links, and ExamsTool to retrieve all available groups, links, and their exams.
Get Group DetailsTool to retrieve detailed information about a specific group.
Get Initial Finished After TimestampTool to compute the initial finishedAfter timestamp.
Get QuestionTool to retrieve a specific question by its ID.
Get Recent Results For Group ExamTool to fetch recent test results for a specific group and exam.
Get Recent Results Link ExamTool to fetch recent results for a specific link and exam.
Get Test DetailsTool to retrieve detailed information for a specific test.
Get User DetailsTool to retrieve detailed information about a specific user.
List AssignmentsTool to list all assignments.
List Question CategoriesTool to retrieve all question categories.
List CertificatesTool to list all certificates.
List GroupsTool to list all groups in your ClassMarker account.
List QuestionsTool to list all questions.
List Recent Results For GroupsTool to retrieve recent exam results for all groups.
List Recent Results for LinksTool to retrieve recent exam results for all links.
List TestsTool to list all available tests.
List UsersTool to list all users.
List WebhooksTool to retrieve all configured webhooks.
Update Sub-CategoryTool to update an existing sub-category.
Update an existing parent categoryTool to update an existing parent category.
Update QuestionTool to update an existing question in the question bank.

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 Classmarker 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 Classmarker.

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 Classmarker Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["classmarker"]
)

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

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.

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Mastra AI

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LlamaIndex

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Use Classmarker MCP with LlamaIndex

CrewAI

CrewAI

Use Classmarker MCP with CrewAI

FAQ

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

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.

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 Classmarker tools.

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

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.

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 Classmarker data and credentials are handled as safely as possible.

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