How to integrate Google Classroom MCP with CrewAI

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Introduction

This guide walks you through connecting Google Classroom to CrewAI 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 CrewAI 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 a Composio API key and configure your Google Classroom connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Google Classroom
  • Build a conversational loop where your agent can execute Google Classroom operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Google Classroom connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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 crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Google Classroom via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

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_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Google Classroom MCP URL

Create a Composio Tool Router session for Google Classroom

python
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:
  • You create a Google Classroom only session through Composio
  • Composio returns an MCP HTTP URL that exposes Google Classroom tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Google Classroom Assistant",
    goal="Help users interact with Google Classroom through natural language commands",
    backstory=(
        "You are an expert assistant with access to Google Classroom tools. "
        "You can perform various Google Classroom operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Google Classroom MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Google Classroom operations.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Google Classroom related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_google_classroom_agent.py

Complete Code

Here's the complete code to get you started with Google Classroom and CrewAI:

python
# file: crewai_google_classroom_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

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 LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Google Classroom assistant agent
    toolkit_agent = Agent(
        role="Google Classroom Assistant",
        goal="Help users interact with Google Classroom through natural language commands",
        backstory=(
            "You are an expert assistant with access to Google Classroom tools. "
            "You can perform various Google Classroom operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Google Classroom operations.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Google Classroom related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Google Classroom through Composio's Tool Router. The agent can perform Google Classroom operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

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

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 CrewAI?

Yes, you can. CrewAI 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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