How to integrate D2lbrightspace MCP with Mastra AI

This guide walks you through connecting D2lbrightspace to Mastra AI 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 Mastra AI 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.

D2lbrightspace logoD2lbrightspace
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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.

45 Tools

Introduction

This guide walks you through connecting D2lbrightspace to Mastra AI 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 Mastra AI 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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TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes D2lbrightspace tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch D2lbrightspace tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your D2lbrightspace agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

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:

  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

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach D2lbrightspace through MCP.
3

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_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 your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models
5

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session
6

Create a Tool Router session for D2lbrightspace

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["d2lbrightspace"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("D2lbrightspace MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "d2lbrightspace" for D2lbrightspace access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the D2lbrightspace toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "d2lbrightspace-mastra-agent",
    instructions: "You are an AI agent with D2lbrightspace tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM
9

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        d2lbrightspace: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and D2lbrightspace toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

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

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["d2lbrightspace"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      d2lbrightspace: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "d2lbrightspace-mastra-agent",
    instructions: "You are an AI agent with D2lbrightspace tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { d2lbrightspace: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with D2lbrightspace through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows
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.

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. Mastra 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 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.

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