Canvas MCP for Educators

What Can Canvas MCP Do for You?

Canvas MCP provides AI-powered assistance for common teaching workflows:

Prerequisites

• Python 3.10+ installed on your computer
• Claude Desktop - Download from claude.ai
• Canvas Account with instructor/TA permissions

Installation

1. Clone and Install

git clone https://github.com/vishalsachdev/canvas-mcp.git
cd canvas-mcp

# Create virtual environment
python3 -m venv .venv
. .venv/bin/activate

# Install
pip install -e .

2. Get Your Canvas API Token

1. Log in to your Canvas account
2. Go to Account → Settings
3. Scroll to Approved Integrations
4. Click + New Access Token
5. Name it "Claude AI Teaching Assistant"
6. Copy the token immediately

3. Configure Environment

cp env.template .env

Edit your .env file:

# Canvas API Configuration
CANVAS_API_TOKEN=your_token_here
CANVAS_API_URL=https://canvas.youruniversity.edu

# Privacy Settings (IMPORTANT for FERPA compliance)
ENABLE_DATA_ANONYMIZATION=true
ANONYMIZATION_DEBUG=false

4. Configure Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

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

5. Test and Start

canvas-mcp-server --test
canvas-mcp-server --config

Restart Claude Desktop to activate the tools.

FERPA Compliance & Privacy

How Data Anonymization Works

When ENABLE_DATA_ANONYMIZATION=true is set, Canvas MCP automatically:

1. Converts student names to anonymous IDs (e.g., "John Smith" → "Student_abc123")
2. Masks email addresses (e.g., "john@university.edu" → "student_abc123@masked")
3. Filters PII from discussion posts and submissions
4. Maintains consistency - same student always gets the same anonymous ID
5. Preserves relationships - you can still identify patterns and trends

De-Anonymization Mapping

The system creates local mapping files for you to correlate anonymous IDs with real students:

local_maps/
└── course_BADM_350_mapping.csv

This CSV maps anonymous IDs back to real names - keep it secure and never commit to version control.

Privacy Best Practices

• Enable anonymization - Always set ENABLE_DATA_ANONYMIZATION=true
• Secure your token - Never share or commit your Canvas API token
• Protect mapping files - Keep local_maps/ folder secure
• Local processing only - All data stays on your machine
• Review before sharing - Ensure student data is anonymous in shared conversations

How to Use Canvas MCP

Quick Start Prompts

Assignment Management

• "Which students haven't submitted Assignment 3 in BADM 350?"
• "Show me submission statistics for the latest assignment"
• "List all assignments in my Spring 2025 courses"

Student Analytics

• "Which students are falling behind in BADM 350?"
• "Show me performance analytics for Assignment 5"
• "Who needs academic support based on recent grades?"

Peer Review Management

• "How many students completed their peer reviews for Assignment 2?"
• "Show me peer review completion analytics"
• "Analyze the quality of peer review comments"

Grading & Rubrics

• "Show me the rubric for Assignment 4"
• "List all rubrics for BADM 350"
• "Create a rubric for the final project"

Communication

• "Send a reminder to students who haven't completed peer reviews"
• "Create an announcement about tomorrow's exam"
• "Message students who are missing Assignment 3"

Available Educator Tools

Assignment Management

• list_assignments - View all assignments for a course
• get_assignment_details - Detailed assignment information
• list_submissions - Student submission status
• get_assignment_analytics - Performance and completion statistics

Grading & Rubrics

• create_rubric - Create new rubrics
• get_rubric_details - View rubric criteria
• associate_rubric - Link rubric to assignment
• grade_submission_with_rubric - Grade using rubric
• bulk_grade_submissions - Batch grading (10+ at once)

Student Analytics

• get_student_analytics - Multi-dimensional performance analysis
• identify_at_risk_students - Flag students needing support
• get_peer_review_completion_analytics - Peer review tracking

Peer Review Management

• list_peer_reviews - View peer review assignments
• get_peer_review_comments - Extract review text and metadata
• analyze_peer_review_quality - Quality metrics and analysis
• identify_problematic_peer_reviews - Flag low-quality reviews

Communication & Messaging

• send_conversation - Send messages to students
• send_peer_review_reminders - Automated reminder workflow
• create_announcement - Post course announcements

Discussion Management

• list_discussion_topics - View discussion forums
• get_discussion_entry_details - Read student posts
• reply_to_discussion_entry - Respond to students
• create_discussion_topic - Start new discussions

Example Workflows

Advanced Features

Automated Peer Review Follow-Up

Run a complete follow-up campaign:

Ask: "Run a peer review follow-up campaign for Assignment 2"

This will:
1. Analyze completion
2. Identify incomplete reviews
3. Send targeted reminders
4. Generate a report

Bulk Grading (Code Execution API)

For large-scale grading operations with custom logic, use the Code Execution API for 99.7% token savings:

await bulkGrade({
  courseIdentifier: "60366",
  assignmentId: "123",
  gradingFunction: (submission) => {
    // Custom grading logic
    return { points: 100, comment: "Great work!" };
  }
});

Troubleshooting

"Connection failed" or "Authentication error"

• Check your Canvas API token in .env
• Verify Canvas URL is correct
• Ensure token has instructor permissions

"No students showing" or "empty results"

• Verify you have instructor/TA role in the course
• Check if students are enrolled
• Ensure assignments have submissions enabled

Anonymization Not Working

• Set ENABLE_DATA_ANONYMIZATION=true in .env
• Restart Canvas MCP server
• Check local_maps/ folder is created

Need More Help?

Open an issue on GitHub for additional support.