How to integrate D2lbrightspace MCP with LangChain

This guide walks you through connecting D2lbrightspace to LangChain 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 LangChain 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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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 LangChain 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 LangChain 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 SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your D2lbrightspace project to Composio
  • Create a Tool Router MCP session for D2lbrightspace
  • Initialize an MCP client and retrieve D2lbrightspace tools
  • Build a LangChain agent that can interact with D2lbrightspace
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
2

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.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with D2lbrightspace functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to D2lbrightspace tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['d2lbrightspace']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to D2lbrightspace tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use D2lbrightspace tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "d2lbrightspace-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our D2lbrightspace MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available D2lbrightspace tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any D2lbrightspace related question or task to the agent.\n");

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

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;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

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

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['d2lbrightspace']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "d2lbrightspace-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any D2lbrightspace related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    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;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with D2lbrightspace through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-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.

FRAMEWORKS

How to build D2lbrightspace MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use D2lbrightspace MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use D2lbrightspace MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use D2lbrightspace MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use D2lbrightspace MCP with Claude Cowork

Codex logo

Codex

Use D2lbrightspace MCP with Codex

OpenClaw logo

OpenClaw

Use D2lbrightspace MCP with OpenClaw

Hermes logo

Hermes

Use D2lbrightspace MCP with Hermes

CLI logo

CLI

Use D2lbrightspace MCP with CLI

Google ADK logo

Google ADK

Use D2lbrightspace MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use D2lbrightspace MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use D2lbrightspace MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use D2lbrightspace MCP with LlamaIndex

CrewAI logo

CrewAI

Use D2lbrightspace MCP with CrewAI

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. LangChain 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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