Canvas Bulk Grading

Grade Canvas LMS assignments efficiently using rubric-based workflows. This skill requires the Canvas MCP server to be running and authenticated with an instructor or TA token.

Prerequisites

• Canvas MCP server running and connected
• Authenticated with an educator (instructor/TA) Canvas API token
• Assignment must exist and have submissions to grade
• Rubric must already be created in Canvas and associated with the assignment (Canvas API cannot reliably create rubrics -- use the Canvas web UI for that)

Workflow

Step 1: Gather Assignment and Rubric Information

Before grading, retrieve the assignment details and its rubric criteria.

get_assignment_details(course_identifier, assignment_id)

Then get the rubric. Use

get_assignment_rubric_details

list_all_rubrics

get_assignment_rubric_details(course_identifier, assignment_id)
list_all_rubrics(course_identifier)
get_rubric_details(course_identifier, rubric_id)

Record the criterion IDs (often prefixed with underscore, e.g.,

_8027

Step 2: List Submissions

Retrieve all student submissions to determine how many need grading:

list_submissions(course_identifier, assignment_id)

Note the

user_id

workflow_state

Step 3: Choose a Grading Strategy

Use this decision tree based on the number of submissions to grade:

How many submissions need grading?
|
+-- 1-9 submissions
|   Use grade_with_rubric (one call per submission)
|
+-- 10-29 submissions
|   Use bulk_grade_submissions (concurrent batch processing)
|   Set max_concurrent: 5, rate_limit_delay: 1.0
|   ALWAYS run with dry_run: true first
|
+-- 30+ submissions OR custom grading logic needed
    Use execute_typescript with bulkGrade function
    99.7% token savings -- grading logic runs locally
    ALWAYS run with dry_run: true first

Strategy A: Single Grading (1-9 submissions)

Call

grade_with_rubric

grade_with_rubric(
  course_identifier,
  assignment_id,
  user_id,
  rubric_assessment: {
    "criterion_id": {
      "points": <number>,
      "rating_id": "<string>",    // optional
      "comments": "<string>"      // optional per-criterion feedback
    }
  },
  comment: "Overall feedback"     // optional
)

Strategy B: Bulk Grading (10-29 submissions)

Always dry run first. Build the grades dictionary mapping each user ID to their grade data, then validate before submitting:

bulk_grade_submissions(
  course_identifier,
  assignment_id,
  grades: {
    "user_id_1": {
      "rubric_assessment": {
        "criterion_id": {"points": 85, "comments": "Good analysis"}
      },
      "comment": "Overall feedback"
    },
    "user_id_2": {
      "grade": 92,
      "comment": "Excellent work"
    }
  },
  dry_run: true,          // VALIDATE FIRST
  max_concurrent: 5,
  rate_limit_delay: 1.0
)

Review the dry run output. If everything looks correct, re-run with

dry_run: false

Strategy C: Code Execution (30+ submissions)

For large classes or custom grading logic, use

execute_typescript

execute_typescript(code: `
  import { bulkGrade } from './canvas/grading/bulkGrade.js';

  await bulkGrade({
    courseIdentifier: "COURSE_ID",
    assignmentId: "ASSIGNMENT_ID",
    gradingFunction: (submission) => {
      // Custom grading logic runs locally -- no token cost
      const notebook = submission.attachments?.find(
        f => f.filename.endsWith('.ipynb')
      );

      if (!notebook) return null; // skip ungraded

      return {
        points: 100,
        rubricAssessment: { "_8027": { points: 100 } },
        comment: "Graded via automated review"
      };
    }
  });
`)

Use

search_canvas_tools("grading", "signatures")

Token Efficiency

The three strategies have very different token costs:

grade_with_rubric

bulk_grade_submissions

execute_typescript

The key insight: as submission count grows, sending grading logic to the server (code execution) is far cheaper than bringing all submission data into the conversation.

Safety Rules

1. Always dry run first. For

   bulk_grade_submissions

   , set

   dry_run: true

   before the real run. Review the output for correctness.
2. Verify the rubric before grading. Confirm criterion IDs, point ranges, and rating IDs match the assignment rubric. Mismatched IDs cause silent failures or incorrect grades.
3. Spot-check before bulk. For Strategy B and C, grade 1-2 submissions manually with

   grade_with_rubric

   first. Verify in Canvas that the grade and rubric feedback appear correctly.
4. Respect rate limits. Use

   max_concurrent: 5

   and

   rate_limit_delay: 1.0

   (1 second between batches). Canvas rate limits are approximately 700 requests per 10 minutes.
5. Do not grade without explicit instructor confirmation. Always present the grading plan (rubric mapping, point values, number of students affected) and wait for approval before submitting grades.

Example Prompts

• "Grade Assignment 5 using the rubric"
• "Show me the rubric for the midterm project and grade all submissions"
• "Bulk grade all ungraded submissions for Assignment 3 -- give full marks on criterion 1 and 80% on criterion 2"
• "How many submissions still need grading for the final paper?"
• "Dry run bulk grading for Assignment 7 so I can review before submitting"
• "Use code execution to grade all 150 homework submissions with custom logic"

Error Recovery

list_assignments

list_all_rubrics