How to integrate D2lbrightspace MCP with Autogen

This guide walks you through connecting D2lbrightspace to AutoGen using the Composio tool router. By the end, you'll have a working D2lbrightspace agent that can create a new quiz for your math course, add a new user to the spring semester, copy an instructor role for a new department through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a D2lbrightspace account through Composio's D2lbrightspace MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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D2lbrightspace

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D2L Brightspace is a learning management system for delivering and managing online courses and assessments. It helps educators streamline digital teaching, assignments, and communication with students.

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Introduction

This guide will help you understand how to give your AutoGen agent real control over a D2lbrightspace account through Composio's D2lbrightspace MCP server.

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

Also integrate D2lbrightspace with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

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 D2lbrightspace
Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
Configure an Autogen AssistantAgent that can call D2lbrightspace tools
Run a live chat loop where you ask the agent to perform D2lbrightspace 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 D2lbrightspace MCP server, and what's possible with it?

The D2lbrightspace MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your D2L Brightspace account. It provides structured and secure access to your LMS, so your agent can perform actions like creating courses, managing quizzes, handling user enrollment, and automating gradebook operations on your behalf.

Automated course creation and management: Instantly create new courses, course offerings, or templates, and streamline updates or deletions without manual intervention.
Quiz and assessment automation: Let your agent set up new quizzes, organize quiz categories, and configure assessment parameters to enhance the learning experience.
Gradebook and feedback management: Effortlessly create, modify, or delete grade objects to keep your course grading up to date and provide prompt feedback to learners.
User enrollment and management: Create new user accounts, manage user roles, and handle enrollment or impersonation tasks to simplify onboarding and administration.
Role and permissions control: Copy existing roles, adjust specific permissions, and fine-tune access for different user groups—all directly through your agent.

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:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS

Prerequisites

You will need:

A Composio API key
An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
A D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["d2lbrightspace"]
    )
    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 D2lbrightspace 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 D2lbrightspace assistant agent with MCP tools
    agent = AssistantAgent(
        name="d2lbrightspace_assistant",
        description="An AI assistant that helps with D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["d2lbrightspace"]
    )
    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 D2lbrightspace assistant agent with MCP tools
        agent = AssistantAgent(
            name="d2lbrightspace_assistant",
            description="An AI assistant that helps with D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

TOOLS

Supported Tools

Every D2lbrightspace action and event your agent gets out of the box.

Copy Role

Creates a new role copied from an existing role in D2L Brightspace.

Create Course Offering

Creates a new course offering in D2L Brightspace.

Create Course Template

Creates a new course template in D2L Brightspace.

Create Grade Object

Creates a new grade object for a particular org unit.

Create Quiz

Creates a new quiz in D2L Brightspace.

Create Quiz Category

Creates a new quiz category in D2L Brightspace.

Create User

Creates a new user entity in D2L Brightspace.

Delete Course Template

Deletes a course template from D2L Brightspace.

Delete Course

Deletes a course offering from D2L Brightspace.

Delete Grade Object

Deletes a specific grade object from an org unit.

Delete Quiz

Deletes a quiz from D2L Brightspace.

Delete Quiz Category

Deletes a quiz category from D2L Brightspace.

Delete User

Deletes a user entity from D2L Brightspace.

Delete User Demographics

Deletes one or more of a particular user's associated demographics entries.

Get Course Offering

Retrieves a specific course offering from D2L Brightspace.

Get Course Template

Retrieves a course template from D2L Brightspace.

Get Course Schema

Retrieves the list of parent org unit type constraints for course offerings.

Get Course Template Schema

Retrieves the list of parent org unit type constraints for course offerings built on this template.

Get Current User Information

Retrieves the current user context's user information from D2L Brightspace.

Get Enrolled Roles

Retrieves a list of all enrolled user roles the calling user can view in an org unit.

Get Grade Access

Retrieves a list of users with access to a specified grade.

Get Grade Object

Retrieves a specific grade object for a particular org unit.

Get Grade Objects

Retrieves all current grade objects for a particular org unit.

Get Grade Setup

Retrieves the grades configuration for an org unit.

Get Grade Statistics

Retrieves statistics for a specified grade item.

Get Org Unit Demographics

Retrieves all demographics entries for users enrolled in a particular org unit.

Get Quiz

Retrieves a specific quiz from an org unit.

Get Quiz Access

Retrieves a list of users with access to a specified quiz.

Get Quiz Attempt

Retrieves a specific quiz attempt.

Get Quiz Attempts

Retrieves a list of attempts for a quiz.

Get Quiz Categories

Retrieves all quiz categories belonging to an org unit.

Get Quiz Category

Retrieves a specific quiz category from an org unit.

Get Quiz Questions

Retrieves all questions in a quiz.

Get Quizzes

Retrieves all quizzes belonging to an org unit.

Get Role by ID

Retrieves a particular user role from D2L Brightspace by its ID.

Get Roles

Retrieves a list of all known user roles in D2L Brightspace.

Get User by ID

Retrieves data for a particular user from D2L Brightspace.

Get Users

Retrieves data for one or more users from D2L Brightspace.

Update Course Offering

Updates an existing course offering in D2L Brightspace.

Update Course Template

Updates an existing course template in D2L Brightspace.

Update Grade Object

Updates a specific grade object.

Update Grade Setup

Updates the grades configuration for an org unit.

Update Quiz

Updates an existing quiz in D2L Brightspace.

Update Quiz Category

Updates an existing quiz category in D2L Brightspace.

Update User

Updates an existing user entity in D2L Brightspace.

FRAMEWORKS

How to build D2lbrightspace MCP Agent with another framework

OpenAI Agents SDK

Use D2lbrightspace MCP with OpenAI Agents SDK

Claude Agent SDK

Use D2lbrightspace MCP with Claude Agent SDK

Claude Code

Use D2lbrightspace MCP with Claude Code

Claude Cowork

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Codex

Use D2lbrightspace MCP with Codex

OpenClaw

Use D2lbrightspace MCP with OpenClaw

Hermes

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CLI

Use D2lbrightspace MCP with CLI

Google ADK

Use D2lbrightspace MCP with Google ADK

LangChain

Use D2lbrightspace MCP with LangChain

Vercel AI SDK

Use D2lbrightspace MCP with Vercel AI SDK

Mastra AI

Use D2lbrightspace MCP with Mastra AI

LlamaIndex

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CrewAI

Use D2lbrightspace MCP with CrewAI

MORE TOOLKITS

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Toolkit marketplace

Canvas

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Blackboard

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Blackboard is a digital learning platform for higher education and schools, offering tools to manage courses, track engagement, and deliver interactive content. It helps institutions improve student outcomes through actionable analytics and in-app guidance.

FAQ

Frequently asked questions

With a standalone D2lbrightspace MCP server, the agents and LLMs can only access a fixed set of D2lbrightspace tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from D2lbrightspace 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 D2lbrightspace tools.

Yes, absolutely. You can configure which D2lbrightspace 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 D2lbrightspace data and credentials are handled as safely as possible.

Start with D2lbrightspace.It takes 30 seconds.

Managed auth, hosted MCP servers, and every D2lbrightspace tool your agent needs.Free to start.

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Customer stories

Teams ship real agents on Composio

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate D2lbrightspace MCP with Autogen

Connect D2lbrightspace without auth hassles

Introduction

Also integrate D2lbrightspace with

TL;DR

What is AutoGen?

What is the D2lbrightspace MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

Supported Tools

How to build D2lbrightspace MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Canvas

Accredible certificates

Api bible

Blackboard

Frequently asked questions

What are the differences in Tool Router MCP and D2lbrightspace MCP?+

Can I use Tool Router MCP with Autogen?+

Can I manage the permissions and scopes for D2lbrightspace while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with D2lbrightspace.It takes 30 seconds.

Teams ship real agents on Composio