canvas-lms-mcp

canvas-lms-mcp

2
educationai_integration

Canvas LMS MCP Server is a minimal server that facilitates the integration of AI systems with the Canvas Learning Management System. It provides access to educational data, supporting tools to list and retrieve courses, assignments, quizzes, and more. It enhances the user experience by integrating with AI-powered IDEs like Cursor.

Canvas LMS MCP Server

smithery badge A minimal Canvas LMS MCP (Machine Conversation Protocol) server for easy access to education data through your Canvas LMS instance. This server provides a bridge between AI systems (like Cursor) and Canvas Learning Management System.

Features

  • List planner items (assignments, quizzes, etc.)
  • Get and list assignments
  • Get and list quizzes
  • Get and list courses
  • Get course syllabus
  • Get course modules
  • List files

Installation

Installing via Smithery

To install Canvas LMS Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @ahnopologetic/canvas-lms-mcp --client claude

Prerequisites

  • Python 3.13+
  • Canvas LMS API token
  • uv package manager (recommended)

Installation Methods

Option 1: Install with uvx (Recommended)

The easiest way to install and run canvas-lms-mcp is using uvx:

uvx canvas-lms-mcp

This will run the server in an isolated environment without installing it permanently.

To install the tool permanently:

uv tool install canvas-lms-mcp
Option 2: Install from Source

  1. Clone the repository:

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

  2. Install with uv:

    # Install uv if you don't have it yet
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create a virtual environment and install dependencies
uv venv
uv pip install -e .

    Alternatively, use traditional methods:

    python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

Configuration

Set the following environment variables:

export CANVAS_API_TOKEN="your_canvas_api_token"
export CANVAS_BASE_URL="https://your-institution.instructure.com"  # Default: https://canvas.instructure.com

You can get your Canvas API token from your Canvas account settings.

Running the Server

Start the server with uv:

uv run src/canvas_lms_mcp/main.py

If installed with uvx tool:

canvas-lms-mcp

By default, the server runs on http://localhost:8000. You can use the FastMCP interface at http://localhost:8000/docs to interact with the API.

Available Tools

The server provides the following tools for interacting with Canvas LMS:

Courses

list_courses

List courses that the user is actively enrolled in.

Parameters:

  • page (optional, default=1): Page number (1-indexed)
  • items_per_page (optional, default=10): Number of items per page
get_course

Get a single course by ID.

Parameters:

  • course_id (required): Course ID
  • include (optional): List of additional data to include
get_course_syllabus

Get a course's syllabus.

Parameters:

  • course_id (required): Course ID
get_course_modules

Get modules for a course.

Parameters:

  • course_id (required): Course ID
  • include (optional): List of additional data to include

Assignments

list_assignments

List assignments for a course.

Parameters:

  • course_id (required): Course ID
  • bucket (required): Filter assignments by ("past", "overdue", "undated", "ungraded", "unsubmitted", "upcoming", "future")
  • order_by (required): Field to order assignments by ("due_at", "position", "name")
  • page (optional, default=1): Page number (1-indexed)
  • items_per_page (optional, default=10): Number of items per page
get_assignment

Get a single assignment by ID.

Parameters:

  • course_id (required): Course ID
  • assignment_id (required): Assignment ID

Quizzes

list_quizzes

List quizzes for a course.

Parameters:

  • course_id (required): Course ID
  • include (optional): List of additional data to include
  • page (optional, default=1): Page number (1-indexed)
  • items_per_page (optional, default=10): Number of items per page
get_quiz

Get a single quiz by ID.

Parameters:

  • course_id (required): Course ID
  • quiz_id (required): Quiz ID

Files

list_files

List files for a course or folder.

Parameters:

  • course_id (optional): Course ID
  • folder_id (optional): Folder ID
  • include (optional): List of additional data to include
  • page (optional, default=1): Page number (1-indexed)
  • items_per_page (optional, default=10): Number of items per page

Planner Items

list_planner_items

List planner items for the authenticated user.

Parameters:

  • start_date (required): Start date in ISO 8601 format
  • end_date (required): End date in ISO 8601 format
  • context_codes (optional): List of context codes (e.g., ["course_123"])
  • page (optional, default=1): Page number (1-indexed)
  • items_per_page (optional, default=10): Number of items per page

Integration with Cursor

Cursor is an AI-powered IDE that can interact with the Canvas LMS MCP server to provide education data directly within your development environment.

Setting Up Cursor Integration

  1. Install the Cursor IDE from https://cursor.sh/

  2. Create a .cursor/mcp.json file in your project directory with the following content:

    {
    "mcpServers": {
        "canvas": {
            "command": "uvx",
            "args": [
                 "canvas-lms-mcp"
            ],
            "env": {
                "CANVAS_API_TOKEN": "your_canvas_api_token",
                "CANVAS_BASE_URL": "https://your-institution.instructure.com"
            }
        }
    }
}

    Replace:

    • your_canvas_api_token with your actual Canvas API token
    • your-institution.instructure.com with your Canvas institution URL

  3. Restart Cursor for the changes to take effect.

Cursor Time Integration (Optional)

You can also integrate a time server for timezone-related queries by adding a "time" server to your mcp.json:

"time": {
    "command": "uvx",
    "args": [
        "mcp-server-time",
        "--local-timezone=America/New_York"
    ]
}

This allows you to use time-related functions with your Canvas data.

Usage Examples

Once connected, you can ask Cursor AI about your Canvas data:

  • "What assignments do I have due next week?"
  • "Show me the syllabus for my Biology course"
  • "List all my upcoming quizzes"
  • "What's on my schedule for tomorrow?"

Example conversation:

YOU: What assignments do I have due soon?

CURSOR: I'll check your upcoming assignments.

Based on your Canvas data, here are your upcoming assignments:
- "Final Project" for CS101 due on December 10, 2023
- "Lab Report #5" for BIOL200 due on December 7, 2023
- "Research Paper" for ENGL301 due on December 15, 2023

Development

For detailed development instructions, please see the file.

License

This project is licensed under the MIT License - see the file for details.