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.

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Prerequisites

• Node.js 18 or higher (or Docker — see below)
• A UBC Canvas personal access token (see below)

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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.

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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

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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 |

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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.

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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.

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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.

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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

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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.