How to integrate Google Classroom MCP with Autogen

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Introduction

This guide walks you through connecting Google Classroom to AutoGen using the Composio tool router. By the end, you'll have a working Google Classroom agent that can list all active courses for this teacher, create a new announcement in math class, get details for course id 12345, delete the announcement about homework due through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Google Classroom account through Composio's Google Classroom 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 Google Classroom
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Google Classroom tools
  • Run a live chat loop where you ask the agent to perform Google Classroom 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 Google Classroom MCP server, and what's possible with it?

The Google Classroom MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Google Classroom account. It provides structured and secure access to your classes, assignments, and announcements, so your agent can list courses, manage announcements, create coursework, and handle classroom organization on your behalf.

  • Course and class management: Effortlessly create, list, or delete courses, and get detailed information about any class you manage or attend.
  • Announcement automation: Let your agent create, update, list, or remove announcements in specific courses—keeping students and teachers in the loop.
  • Coursework material handling: Quickly list all coursework materials in a class, so you can track resources and assignments with ease.
  • Streamlined assignment workflows: Organize and distribute assignments and resources, helping automate typical classroom tasks for educators and students.
  • Classroom insights retrieval: Fetch up-to-date details about classes and their structure, enabling your agent to provide summaries or help with enrollment decisions.

Supported Tools & Triggers

Tools
List CourseWorkMaterialsTool to list courseworkmaterials in a course.
Create AnnouncementTool to create an announcement in a course.
Delete AnnouncementTool to delete an announcement.
Get AnnouncementTool to get an announcement.
List AnnouncementsTool to list announcements in a course.
Patch AnnouncementTool to update fields of an announcement.
Create CourseTool to create a new course.
Delete CourseTool to delete a course.
Get CourseTool to get details for a specific course.
List CoursesTool to list all courses accessible to the authenticated user.
Patch CourseTool to update one or more fields of a classroom course.
List Student GuardiansTool to list guardians of a student in a course.
List Course StudentsTool to list students in a course.
Get TeacherTool to get teacher enrollment.
List Course TeachersTool to list teachers in a course.
Create Course TopicTool to create a course topic.
Delete Course TopicTool to delete a course topic.
Get Course TopicTool to get a course topic.
List Course TopicsTool to list topics in a course.
Patch Course TopicTool to update fields of a course topic.
Create CourseWorkTool to create a coursework item in a course.
Delete CourseWorkTool to delete a specific coursework.
Get CourseWorkTool to get details of a specific coursework.
List CourseWorkTool to list coursework in a course.
Create Course Work MaterialTool to create course work material.
Get Coursework MaterialTool to get a coursework material.
List CourseWorkMaterialsTool to list course work materials in a course.
Patch CourseworkTool to update fields of a coursework.
List Student SubmissionsTool to list student submissions for a specific coursework.
Reclaim Student SubmissionTool to reclaim a student submission for editing.
Create InvitationTool to create an invitation for a user to a course.

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

How to build Google Classroom MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Google Classroom MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Google Classroom MCP with Claude Agent SDK

Google ADK

Google ADK

Use Google Classroom MCP with Google ADK

LangChain

LangChain

Use Google Classroom MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Google Classroom MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Google Classroom MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Google Classroom MCP with LlamaIndex

CrewAI

CrewAI

Use Google Classroom MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Google Classroom MCP?

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

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

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

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