Canvas LMS MCP Server

A Model Context Protocol (MCP) server that connects Large Language Models (LLMs) to Canvas LMS APIs, enabling AI agents to manage courses, assignments, student submissions, discussions, announcements, and more.

Features

• Course Management: List courses, search users, inspect sections, and check course lists.
• Assignment Operations: Retrieve assignments, create/update assignments, manage override dates, and assign peer reviews.
• Submissions & Grading: Retrieve student submissions, submit files/grades, add comments, and handle role-adaptive workflows (student vs. educator).
• Discussions & Announcements: Create, read, and moderate discussion topics and announcements.
• Calendar & Planning: Fetch planner items, calendar events, and blackout dates.

Prerequisites

• Node.js: Version 18 or higher.
• Canvas API Token: Generate a token from your Canvas instance under Account > Settings > Approved Integrations > New Access Token.
• Canvas Base URL: The URL of your Canvas instance (e.g., https://canvas.instructure.com or https://<your-school>.instructure.com).

Configuration & Usage

The easiest way to run the MCP server is via npx, which downloads and runs the server automatically.

1. Claude Desktop

Add the following configuration to your Claude Desktop configuration file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "canvas-lms": {
      "command": "npx",
      "args": [
        "-y",
        "@likr/canvas-lms-mcp"
      ],
      "env": {
        "CANVAS_BASE_URL": "https://canvas.instructure.com",
        "CANVAS_API_TOKEN": "YOUR_CANVAS_API_TOKEN_HERE"
      }
    }
  }
}

2. Cursor

1. Open Cursor and go to Settings > Features > MCP.
2. Click + Add New MCP Server.
3. Configure the following:
   • Name: canvas-lms
   • Type: command
   • Command: npx -y @likr/canvas-lms-mcp
4. Set the following Environment Variables in the Cursor interface:
   • CANVAS_BASE_URL: https://canvas.instructure.com (or your custom instance URL)
   • CANVAS_API_TOKEN: YOUR_CANVAS_API_TOKEN_HERE

3. Cline / Roo-Code

Add the server configuration to your Cline MCP settings file (typically at ~/.code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json or similar):

{
  "mcpServers": {
    "canvas-lms": {
      "command": "npx",
      "args": [
        "-y",
        "@likr/canvas-lms-mcp"
      ],
      "env": {
        "CANVAS_BASE_URL": "https://canvas.instructure.com",
        "CANVAS_API_TOKEN": "YOUR_CANVAS_API_TOKEN_HERE"
      }
    }
  }
}

Canvas LMS Skill

This repository provides a dedicated canvas-lms skill for AI agents (supporting Antigravity or standard skill configurations). This skill provides the agent with structured best practices, role-adaptive behaviors (student vs. educator workflows), and reference guides to interact with the Canvas LMS APIs effectively.

To install this skill in your agent workspace, run:

npx skills add likr/canvas-lms-agent@canvas-lms

Environment Variables

| Variable | Description | Default | | --- | --- | --- | | CANVAS_BASE_URL | The domain of your Canvas LMS instance (e.g. https://canvas.instructure.com). | https://canvas.instructure.com | | CANVAS_API_TOKEN | Your Canvas API personal access token. (Required) | None |

Development

If you wish to run the server locally for development:

1. Clone the repository and navigate to the directory:

   git clone https://github.com/likr/canvas-lms-agent.git
   cd canvas-lms-agent/canvas-lms-mcp

2. Install dependencies:

   npm install

3. Set up environment variables in a .env file:

   CANVAS_BASE_URL=https://canvas.instructure.com
   CANVAS_API_TOKEN=your_token_here

4. Start the server:

   npm start

5. Run tests:

   npm test

License

This project is licensed under the MIT License. See the LICENSE file for details.