canvas-mcp-lite

A lean, instructor-focused MCP server for Canvas LMS, built with FastMCP. It lets an AI assistant act as a teaching assistant against your Canvas instance: browsing courses, reading student submissions (including PDF/DOCX file uploads), grading with rubrics, posting announcements, managing modules and pages, and more.

Tools

64 tools across 11 Canvas domains, organized by risk level:

Group	Count	Examples
Read	32	list_courses, list_ungraded_submissions, list_missing_submissions, get_submission_content, list_quiz_submissions, get_student_analytics
Write	23	create_assignment, grade_submission, grade_with_rubric, post_grades, create_announcement, send_message, upload_course_file
Delete	9	delete_assignment, delete_page, bulk_delete_announcements

Highlights:

• Read what students actually submitted — get_submission_content extracts text from uploaded PDFs, DOCX files, and plain text (plus typed text entries, URLs, and discussion submissions) and includes the submission comment thread, so grading tools work from real content.
• A real grading workflow — list_ungraded_submissions is the grading queue, grade_submission handles points, pass/fail, letter, and percent grades (and reports partial success when Canvas saves a comment but rejects a grade), and post_grades/hide_grades control when students see results.
• Course codes or IDs — every course-scoped tool accepts either a numeric course ID or a course_code string (resolved with a short-TTL cache).
• Safe by default — assignments and pages are created unpublished unless you say otherwise; destructive tools are clearly marked.
• LLM-friendly output — every tool returns formatted, readable text rather than raw JSON.

Setup

Requires Python 3.10+.

1. Install (editable, from the project root):

   python -m venv .venv
   source .venv/bin/activate
   pip install -e .
   

2. Configure credentials in a .env file next to the package:

   CANVAS_API_URL=https://yourschool.instructure.com/api/v1
   CANVAS_API_TOKEN=your-canvas-access-token
   

   Generate a token in Canvas under Account → Settings → New Access Token. The .env is loaded relative to the package location (not the working directory), because MCP clients launch servers from arbitrary directories.

3. Run via the console script:

   canvas-mcp-lite
   

Using with Claude

Register it as an stdio MCP server. For Claude Code:

claude mcp add canvas -- /path/to/.venv/bin/canvas-mcp-lite

Or in a Claude Desktop / MCP client config:

{
  "mcpServers": {
    "canvas": {
      "command": "/path/to/.venv/bin/canvas-mcp-lite"
    }
  }
}

Project layout

canvas_mcp_lite/
  server.py      FastMCP entry point; registers READ/WRITE/DELETE tool lists
  client.py      Async Canvas API client: auth, pagination, retry, errors
  util.py        Course code → ID resolution (cached), date formatting
  tools/         One module per domain: courses, assignments, grading,
                 modules_pages, announcements, discussions, files, quizzes,
                 messaging, peer_review, analytics

Notes

• The package name is canvas_mcp_lite and it uses relative imports — run it through the installed canvas-mcp-lite script, not python server.py.
• File downloads for text extraction are capped at 25MB; scanned/image-only PDFs return a notice instead of text.
• Canvas API errors surface with status code and URL; transient timeouts are retried with backoff.