How to integrate Classmarker MCP with Pydantic AI

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Introduction

This guide walks you through connecting Classmarker to Pydantic AI 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agent that can add student to biology exam access list, create a new question for math quiz, delete user account for withdrawn student, organize new hires into onboarding group through natural language commands.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 Pydantic AI 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:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Classmarker
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Classmarker workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming

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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Classmarker
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Classmarker
  • MCPServerStreamableHTTP connects to the Classmarker MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Classmarker
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["classmarker"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Classmarker tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
classmarker_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[classmarker_mcp],
    instructions=(
        "You are a Classmarker assistant. Use Classmarker tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Classmarker endpoint
  • The agent uses GPT-5 to interpret user commands and perform Classmarker operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Classmarker.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Classmarker API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Classmarker and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Classmarker
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["classmarker"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    classmarker_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[classmarker_mcp],
        instructions=(
            "You are a Classmarker assistant. Use Classmarker tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Classmarker.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Classmarker through Composio's Tool Router. With this setup, your agent can perform real Classmarker actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Classmarker for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

How to build Classmarker MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Classmarker MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Classmarker MCP with Claude Agent SDK

Google ADK

Google ADK

Use Classmarker MCP with Google ADK

LangChain

LangChain

Use Classmarker MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Classmarker MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Classmarker MCP with Mastra AI

LlamaIndex

LlamaIndex

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 Pydantic AI?

Yes, you can. Pydantic AI 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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