Canvas LMS MCP

Canvas LMS MCP Server

A Python MCP server that exposes Canvas LMS academic data as callable tools for AI agents.

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What is MCP?

Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI models communicate with external tools and data sources. Instead of embedding all logic inside a prompt, an AI agent discovers available tools at runtime and calls them by name with typed arguments — just like a function call.

MCP separates concerns cleanly:

AI Agent  ──(tool call)──►  MCP Server  ──(HTTP)──►  Canvas LMS API
           ◄──(result)────              ◄──(JSON)────

The agent never knows or cares how Canvas authentication works; it only calls get_courses() and receives structured data.

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Architecture

canvas-mcp/
├── main.py           # MCP server — registers tools and starts the server
├── canvas_client.py  # Canvas API client — authentication, pagination, retries
├── requirements.txt
├── .env.example
└── README.md

canvas_client.py — CanvasClient

The CanvasClient class centralises all HTTP communication with Canvas:

| Concern | Implementation | |---|---| | Authentication | Authorization: Bearer <token> header on every request | | Pagination | Parses Link: <url>; rel="next" headers and accumulates pages automatically | | Retries | urllib3.Retry with 3 attempts, exponential backoff, on 429/5xx responses | | Error surface | All failures raise CanvasAPIError with the HTTP status and body |

main.py — FastMCP Server

Uses FastMCP from the mcp package to register Python functions as MCP tools. Each tool:

1. Calls one or more CanvasClient methods.
2. Filters the raw Canvas response to the fields the agent actually needs.
3. Returns a plain Python list[dict] or dict — always JSON-serialisable.

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Canvas Integration

Authentication

Canvas uses Personal Access Tokens for API authentication. Once generated in Canvas Settings, the token is sent as a Bearer token on every request:

Authorization: Bearer <CANVAS_API_TOKEN>

Endpoints Used

| MCP Tool | Canvas Endpoint | |---|---| | get_courses | GET /api/v1/courses | | get_assignments | GET /api/v1/courses/{id}/assignments | | get_upcoming_events | GET /api/v1/planner/items | | get_course_grades | GET /api/v1/courses/{id}/enrollments | | get_course_summary | Combines courses + assignments + enrollments |

Pagination

Canvas paginates results via Link response headers:

Link: <https://canvas.example.com/api/v1/courses?page=2>; rel="next"

CanvasClient._get() follows these automatically, collecting every page into a single list before returning.

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Installation

Prerequisites

• Python 3.11 or later
• A Canvas LMS account with API access

Steps

# 1. Clone / enter the project
cd canvas-mcp

# 2. Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Configure environment variables
cp .env.example .env
# Edit .env with your Canvas URL and token

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Configuration

Copy .env.example to .env and fill in your values:

# Your institution's Canvas domain
CANVAS_BASE_URL=https://canvas.example.edu

# Personal Access Token from Canvas Account → Settings → New Access Token
CANVAS_API_TOKEN=your_token_here

The server validates both variables at startup and exits with a clear error message if either is missing.

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Running the MCP Server

# Activate venv if not already active
source .venv/bin/activate

# Start the server (communicates over stdio)
python main.py

The server speaks the MCP stdio transport. To wire it into Claude Code, add it to your .claude/settings.json:

{
  "mcpServers": {
    "canvas": {
      "command": "python",
      "args": ["/absolute/path/to/canvas-mcp/main.py"],
      "env": {
        "CANVAS_BASE_URL": "https://canvas.example.edu",
        "CANVAS_API_TOKEN": "your_token_here"
      }
    }
  }
}

Or set CANVAS_BASE_URL / CANVAS_API_TOKEN in your shell environment and omit the env block.

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Available MCP Tools

get_courses()

Returns all active courses for the authenticated user.

get_assignments(course_id)

Returns all assignments for the specified course.

get_upcoming_events()

Returns upcoming items from the Canvas Planner (assignments, calendar events, etc.).

get_course_grades(course_id)

Returns current score and letter grade for every enrolled student in the course.

get_course_summary(course_id)

Aggregates course info, assignments, and grades into a single summary including:

• Total assignments
• Next upcoming assignment
• Number of enrolled students
• Average score and letter grade

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Example Agent Prompts

List my available courses.

Show assignments for course 123.

What deadlines do I have this week?

Show grade information for course 123.

Generate a summary of course 123.

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Error Handling

• All tools catch CanvasAPIError and return {"error": "..."} rather than raising, so the agent can surface the message gracefully.
• HTTP errors (4xx/5xx) include the Canvas response body in the error message.
• Transient failures (429, 502, 503, 504) are retried automatically up to 3 times with exponential backoff.