Skip to main content
Glama
terraceonhigh

Canvas MCP Server

by terraceonhigh
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Canvas MCP Server

An MCP (Model Context Protocol) server that exposes UBC Canvas LMS data as tools consumable by Claude Desktop, Claude Code, n8n AI Agent nodes, or any MCP-compatible client.

  AI Agent / Claude Desktop / n8n
           │ MCP tool calls
           ▼
     canvas-mcp (this server)
           │ REST
           ▼
 https://ubc.instructure.com/api/v1/

This server is read-only. It surfaces Canvas data; it never writes, drafts, or submits anything.

Prerequisites

  • Node.js 18 or higher (or Docker — see below)

  • A UBC Canvas personal access token (see below)

Related MCP server: Canvas MCP

Token Generation

  1. Log in to Canvas at https://ubc.instructure.com

  2. Click your avatar (top-left) → Account → Settings

  3. Scroll to Approved Integrations → click + New Access Token

  4. Give it a descriptive purpose (e.g. "MCP Server")

  5. Copy the token — it will not be shown again

Security note: This token can read your grades, submissions, and course communications. Never share it, never commit it, and revoke it immediately if your machine is compromised.

Setup

# 1. Clone the repository
git clone <repo-url>
cd Canvas-MCP-Server

# 2. Install dependencies
npm install

# 3. Configure environment
cp .env.example .env
# Edit .env and paste your token:
#   CANVAS_TOKEN=your_token_here
#   CANVAS_BASE_URL=https://ubc.instructure.com/api/v1

# 4. Build
npm run build

# 5. Verify your token works
echo '{"jsonrpc":"2.0","method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}},"id":1}' | node dist/index.js

Available Tools

Tool

Description

whoami

Verify your token and return your Canvas profile (name, email, login ID)

list_courses

List all active courses. Returns course IDs required by other tools

list_assignments

List upcoming assignments for a course (default: next 14 days, max 90)

get_assignment

Full assignment details with plain-text description (HTML stripped)

list_announcements

Recent announcements from one or more courses (default: last 14 days)

list_upcoming

Unified calendar view of upcoming assignment due dates across all courses

get_submission

Your submission status for an assignment: score, grade, late/missing flags

Fresh Ubuntu Setup (Docker)

Follow these three steps on a brand-new Ubuntu machine. No Node.js installation needed.

Step 1 — Install Docker Engine

sudo apt-get update
sudo apt-get install -y ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc
echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] \
  https://download.docker.com/linux/ubuntu $(. /etc/os-release && echo "$VERSION_CODENAME") stable" \
  | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

Allow running Docker without sudo:

sudo usermod -aG docker $USER
newgrp docker           # apply group change in current shell without logging out
docker run hello-world  # confirm Docker is working

Step 2 — Clone the repo and set your token

git clone https://github.com/terraceonhigh/Canvas-MCP-Server.git
cd Canvas-MCP-Server
cp .env.example .env
nano .env   # paste your CANVAS_TOKEN value

Step 3 — Build the image and verify

docker build -t canvas-mcp .

# Smoke-test: should print a valid JSON-RPC response to stdout
echo '{"jsonrpc":"2.0","method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}},"id":1}' \
  | docker run --rm -i \
    -e CANVAS_TOKEN=$(grep CANVAS_TOKEN .env | cut -d= -f2) \
    -e CANVAS_BASE_URL=https://ubc.instructure.com/api/v1 \
    canvas-mcp

The token is read from .env only for this test command and passed via -e at runtime — it is never baked into the image.

Docker

You can run the server in a container instead of installing Node.js locally.

# Build the image
docker build -t canvas-mcp .

# Verify your token (one-shot test)
echo '{"jsonrpc":"2.0","method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}},"id":1}' \
  | docker run --rm -i \
    -e CANVAS_TOKEN=your_token_here \
    -e CANVAS_BASE_URL=https://ubc.instructure.com/api/v1 \
    canvas-mcp

The container reads MCP messages from stdin and writes responses to stdout, so always pass -i to docker run. Never use -t (TTY), as it would corrupt the binary stdio stream.

Claude Desktop with Docker

{
  "mcpServers": {
    "canvas": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CANVAS_TOKEN=your_token_here",
        "-e", "CANVAS_BASE_URL=https://ubc.instructure.com/api/v1",
        "canvas-mcp"
      ]
    }
  }
}

Claude Desktop Configuration

Add this to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "canvas": {
      "command": "node",
      "args": ["dist/index.js"],
      "cwd": "/absolute/path/to/Canvas-MCP-Server",
      "env": {
        "CANVAS_TOKEN": "your_token_here",
        "CANVAS_BASE_URL": "https://ubc.instructure.com/api/v1"
      }
    }
  }
}

Replace /absolute/path/to/Canvas-MCP-Server with the actual path on your machine.

n8n AI Agent Node

In any n8n AI Agent node:

  1. Add a new MCP Tool credential

  2. Set the command to node and args to ["dist/index.js"]

  3. Set the working directory to this project's path

  4. Set CANVAS_TOKEN and CANVAS_BASE_URL as environment variables

The agent can then call tools like list_assignments, filter results, and pipe them to downstream services (task managers, calendars, notification systems) in the same workflow.

Architecture

src/
├── index.ts            # MCP server entry point — registers all tools
├── canvas-client.ts    # Authenticated fetch wrapper with automatic pagination
└── tools/
    ├── courses.ts      # list_courses
    ├── assignments.ts  # list_assignments, get_assignment
    ├── announcements.ts# list_announcements
    ├── calendar.ts     # list_upcoming
    └── submissions.ts  # get_submission, whoami

Key design decisions:

  • Pagination is automatic — all list endpoints follow Link: rel="next" headers until exhausted

  • HTML is always stripped — descriptions and announcement bodies are returned as clean plain text

  • Token is never logged — the Authorization header is excluded from all error messages and debug output

  • Stdout is sacred — all diagnostic output goes to stderr; stdout carries only MCP protocol messages

Known Limitations

  • Rate limits: Canvas enforces undocumented rate limits. Polling workflows should add delays between requests if 403s appear.

  • Token scope: A personal token reads everything you can see in Canvas. Treat it like a password.

  • Announcement interpretation: Pattern-matching (e.g. detecting class cancellations) is the consuming agent's job — this server returns clean text only.

This server cannot be installed

A
license - permissive license
-
quality - not tested
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/terraceonhigh/Canvas-MCP-Server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server