How to integrate D2lbrightspace MCP with CrewAI

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Introduction

This guide walks you through connecting D2lbrightspace to CrewAI using the Composio tool router. By the end, you'll have a working D2lbrightspace agent that can create a new quiz for my math course, add a new user to the spring semester, copy an instructor role for a new department, delete an outdated course template from the system through natural language commands.

This guide will help you understand how to give your CrewAI 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.

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your D2lbrightspace connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for D2lbrightspace
  • Build a conversational loop where your agent can execute D2lbrightspace 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 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.

Supported Tools & Triggers

Tools
Copy RoleCreates a new role copied from an existing role in d2l brightspace.
Create Course OfferingCreates a new course offering in d2l brightspace.
Create Course TemplateCreates a new course template in d2l brightspace.
Create Grade ObjectCreates a new grade object for a particular org unit.
Create QuizCreates a new quiz in d2l brightspace.
Create Quiz CategoryCreates a new quiz category in d2l brightspace.
Create UserCreates a new user entity in d2l brightspace.
Delete Course TemplateDeletes a course template from d2l brightspace.
Delete CourseDeletes a course offering from d2l brightspace.
Delete Grade ObjectDeletes a specific grade object from an org unit.
Delete QuizDeletes a quiz from d2l brightspace.
Delete Quiz CategoryDeletes a quiz category from d2l brightspace.
Delete UserDeletes a user entity from d2l brightspace.
Delete User DemographicsDeletes one or more of a particular user's associated demographics entries.
Get Course OfferingRetrieves a specific course offering from d2l brightspace.
Get Course TemplateRetrieves a course template from d2l brightspace.
Get Course SchemaRetrieves the list of parent org unit type constraints for course offerings.
Get Course Template SchemaRetrieves the list of parent org unit type constraints for course offerings built on this template.
Get Current User InformationRetrieves the current user context's user information from d2l brightspace.
Get Enrolled RolesRetrieves a list of all enrolled user roles the calling user can view in an org unit.
Get Grade AccessRetrieves a list of users with access to a specified grade.
Get Grade ObjectRetrieves a specific grade object for a particular org unit.
Get Grade ObjectsRetrieves all current grade objects for a particular org unit.
Get Grade SetupRetrieves the grades configuration for an org unit.
Get Grade StatisticsRetrieves statistics for a specified grade item.
Get Org Unit DemographicsRetrieves all demographics entries for users enrolled in a particular org unit.
Get QuizRetrieves a specific quiz from an org unit.
Get Quiz AccessRetrieves a list of users with access to a specified quiz.
Get Quiz AttemptRetrieves a specific quiz attempt.
Get Quiz AttemptsRetrieves a list of attempts for a quiz.
Get Quiz CategoriesRetrieves all quiz categories belonging to an org unit.
Get Quiz CategoryRetrieves a specific quiz category from an org unit.
Get Quiz QuestionsRetrieves all questions in a quiz.
Get QuizzesRetrieves all quizzes belonging to an org unit.
Get Role by IDRetrieves a particular user role from d2l brightspace by its id.
Get RolesRetrieves a list of all known user roles in d2l brightspace.
Get User by IDRetrieves data for a particular user from d2l brightspace.
Get UsersRetrieves data for one or more users from d2l brightspace.
Update Course OfferingUpdates an existing course offering in d2l brightspace.
Update Course TemplateUpdates an existing course template in d2l brightspace.
Update Grade ObjectUpdates a specific grade object.
Update Grade SetupUpdates the grades configuration for an org unit.
Update QuizUpdates an existing quiz in d2l brightspace.
Update Quiz CategoryUpdates an existing quiz category in d2l brightspace.
Update UserUpdates an existing user entity in d2l brightspace.

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 D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace MCP URL

Create a Composio Tool Router session for D2lbrightspace

python
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:
  • You create a D2lbrightspace only session through Composio
  • Composio returns an MCP HTTP URL that exposes D2lbrightspace 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="D2lbrightspace Assistant",
    goal="Help users interact with D2lbrightspace through natural language commands",
    backstory=(
        "You are an expert assistant with access to D2lbrightspace tools. "
        "You can perform various D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace 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_d2lbrightspace_agent.py

Complete Code

Here's the complete code to get you started with D2lbrightspace and CrewAI:

python
# file: crewai_d2lbrightspace_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 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 LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create D2lbrightspace assistant agent
    toolkit_agent = Agent(
        role="D2lbrightspace Assistant",
        goal="Help users interact with D2lbrightspace through natural language commands",
        backstory=(
            "You are an expert assistant with access to D2lbrightspace tools. "
            "You can perform various D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace 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 D2lbrightspace through Composio's Tool Router. The agent can perform D2lbrightspace 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 D2lbrightspace MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

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Claude Agent SDK

Claude Agent SDK

Use D2lbrightspace MCP with Claude Agent SDK

Google ADK

Google ADK

Use D2lbrightspace MCP with Google ADK

LangChain

LangChain

Use D2lbrightspace MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use D2lbrightspace MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use D2lbrightspace MCP with Mastra AI

LlamaIndex

LlamaIndex

Use D2lbrightspace MCP with LlamaIndex

FAQ

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

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.

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 D2lbrightspace tools.

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

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.

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 D2lbrightspace data and credentials are handled as safely as possible.

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