Canvas MCP for Educators

Welcome! This guide will help you set up Canvas MCP to enhance your teaching with AI-powered course management tools.

What Can Canvas MCP Do for You?

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

Assignment Management: Track submissions, identify missing work, analyze completion rates
Grading & Rubrics: Manage rubrics, grade submissions, provide feedback
Student Analytics: Monitor student performance, identify at-risk students, track engagement
Peer Review Management: Track peer review completion, analyze review quality, send reminders
Discussion Facilitation: Monitor discussions, respond to students, analyze participation
Communication: Send targeted messages, create announcements, automate reminders
FERPA Compliance: Built-in data anonymization for AI-safe student analytics

Prerequisites

Python 3.10+ installed on your computer
MCP Client - Any MCP-compatible client:
- Claude Desktop (Recommended for beginners)
- Cursor (AI code editor)
- Zed (Fast code editor)
- Windsurf (AI-powered IDE)
- Continue (Open-source AI assistant)
- Other MCP clients
Canvas Account with instructor/TA permissions

Installation

1. Clone the Repository

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

2. Install Dependencies

# Install uv package manager (faster than pip)
pip install uv

# Install Canvas MCP
uv pip install -e .

3. Get Your Canvas API Token

Log in to your Canvas account
Go to Account → Settings
Scroll down to Approved Integrations
Click + New Access Token
Give it a purpose (e.g., "AI Teaching Assistant")
Click Generate Token
Copy the token immediately - you won't see it again!

4. Configure Canvas MCP

Create a .env file in the canvas-mcp directory:

# Copy the template
cp env.template .env

# Edit the .env file and add your credentials

Your .env file should look like this:

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

# MCP Server Configuration (optional)
MCP_SERVER_NAME=canvas-mcp

# Privacy Settings (IMPORTANT for FERPA compliance)
ENABLE_DATA_ANONYMIZATION=true  # Anonymizes student data before AI processing
ANONYMIZATION_DEBUG=false       # Set to true for debugging only

# Optional: Institution name for display
INSTITUTION_NAME=Your University Name

Important Configuration Notes:

Replace https://canvas.youruniversity.edu with your actual Canvas URL
Set ENABLE_DATA_ANONYMIZATION=true for FERPA-compliant student data handling
The anonymization system converts student names to anonymous IDs (e.g., "Student_abc123") before sending data to AI

5. Configure Your MCP Client

Choose your MCP client and follow the appropriate configuration:

Claude Desktop (Recommended for beginners)

Configuration file location:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Add this configuration:

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

macOS path example: /Users/yourname/canvas-mcp/.venv/bin/canvas-mcp-server
Windows path example: C:\Users\yourname\canvas-mcp\.venv\Scripts\canvas-mcp-server.exe

Tip: Point your client at the absolute path to your virtualenv binary to avoid pyenv shim/path issues.

Cursor

Configuration file location:

macOS/Linux: ~/.cursor/mcp_config.json
Windows: %USERPROFILE%\.cursor\mcp_config.json

Configuration:

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

Zed

Add to Zed's settings.json (Settings menu → Open Settings):

{
  "context_servers": {
    "canvas-api": {
      "command": {
        "path": "/absolute/path/to/canvas-mcp/.venv/bin/canvas-mcp-server",
        "args": []
      }
    }
  }
}

Windsurf IDE

Configuration file location:

macOS: ~/Library/Application Support/Windsurf/mcp_config.json
Windows: %APPDATA%\Windsurf\mcp_config.json

Configuration:

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

Continue

Add to Continue's config.json (Continue settings):

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

Other MCP Clients

For other MCP-compatible clients:

Locate your client's MCP configuration file
Add a server entry with the command path to canvas-mcp-server
Restart your client

Consult your client's documentation for specific configuration format.

6. Test Your Setup

# Test the Canvas API connection
canvas-mcp-server --test

# View your configuration
canvas-mcp-server --config

You should see: ✓ API connection successful!

7. Restart Your MCP Client

Close and reopen your MCP client to load the Canvas MCP server.

Verification:

Claude Desktop: Look for the 🔨 hammer icon when you start a conversation
Other clients: Check your client's documentation for how MCP tools are indicated

FERPA Compliance & Privacy

How Data Anonymization Works

When ENABLE_DATA_ANONYMIZATION=true is set, Canvas MCP automatically:

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

De-Anonymization Mapping

The system creates local mapping files that let you correlate anonymous IDs with real students:

local_maps/
└── course_BADM_350_mapping.csv

This CSV file 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 (it's in .gitignore)
Local processing only - All data stays on your machine; nothing sent to external servers
Review before sharing - If sharing AI assistant conversations, ensure student data is anonymous

How to Use Canvas MCP

Quick Start Prompts for Educators

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"
"Identify students who haven't completed peer reviews"
"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" (provide criteria)

Discussion Facilitation:

"What are the active discussions in my course?"
"Show me recent student posts in the Week 5 discussion"
"Which students haven't participated in discussions?"

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"

Understanding Tool Calls

When you ask your AI assistant a question, it uses various "tools" to fetch data from Canvas. For educators, common tools include:

get_assignment_analytics - Submission statistics and performance
list_submissions - Student submission status
get_peer_review_completion_analytics - Peer review tracking
send_peer_review_reminders - Automated student messaging
list_discussion_topics - Discussion management
create_rubric - Rubric creation

Note: Different MCP clients may display tool usage differently. Claude Desktop shows a 🔨 icon when using tools.

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

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
assign_peer_review - Manually assign reviews

Communication & Messaging

send_conversation - Send messages to students
send_peer_review_reminders - Automated reminder workflow
create_announcement - Post course announcements
send_peer_review_followup_campaign - Complete analytics → messaging pipeline

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

Course Content

list_courses - View all your courses
get_course_details - Syllabus and course info
list_pages - Access course pages
get_page_content - Read page content

Example Workflows

Monday Morning Check-In

You: "Give me a status update on my courses"

Your AI assistant will:
1. List your active courses
2. Check recent assignment submissions
3. Identify missing work
4. Flag students needing attention

After Assignment Due Date

You: "Assignment 3 was due Friday in BADM 350. Who hasn't submitted?"

Your AI assistant will:
1. Get submission statistics
2. List non-submitters (anonymized if enabled)
3. Suggest sending reminders

Peer Review Management

You: "Check peer review completion for Assignment 2 in BADM 350"

Your AI assistant will:
1. Analyze completion rates
2. Identify incomplete reviews
3. Assess review quality
4. Suggest follow-up actions (reminders, manual assignments)

Grading Session

You: "Show me the rubric for Assignment 4 and recent submissions"

Your AI assistant will:
1. Display rubric criteria
2. Show submission list
3. Help you grade efficiently

Tips for Best Results

Use course codes: Be specific (e.g., "BADM 350" instead of "my course")
Combine requests: "Show submissions and analytics for Assignment 3"
Ask for summaries: "Summarize student performance in BADM 350"
Leverage anonymization: Work confidently knowing student data is protected
Automate repetitive tasks: Use messaging tools for reminders and follow-ups

Advanced Features

Automated Peer Review Follow-Up

You: "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

Student Performance Analysis

You: "Analyze student performance trends in BADM 350"

Your AI assistant can:
- Identify struggling students
- Track assignment completion patterns
- Suggest interventions
- Generate support lists

Bulk Communication

You: "Message all students who haven't submitted Assignment 3"

Your AI assistant will:
- Identify non-submitters
- Draft appropriate message
- Send bulk communication

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
Check the main README for setup help
Review CLAUDE.md for technical details

Contributing

We welcome contributions! See the main README for guidelines.

Support

For questions or issues:

GitHub Issues: canvas-mcp/issues
Documentation: See README.md and CLAUDE.md

Happy teaching! 🎓

FilesExpand file tree

EDUCATOR_GUIDE.md

Latest commit

History

EDUCATOR_GUIDE.md

File metadata and controls

Canvas MCP for Educators

What Can Canvas MCP Do for You?

Prerequisites

Installation

1. Clone the Repository

2. Install Dependencies

3. Get Your Canvas API Token

4. Configure Canvas MCP

5. Configure Your MCP Client

6. Test Your Setup

7. Restart Your MCP Client

FERPA Compliance & Privacy

How Data Anonymization Works

De-Anonymization Mapping

Privacy Best Practices

How to Use Canvas MCP

Quick Start Prompts for Educators

Understanding Tool Calls

Available Educator Tools

Assignment Management

Grading & Rubrics

Student Analytics

Peer Review Management

Communication & Messaging

Discussion Management

Course Content

Example Workflows

Monday Morning Check-In

After Assignment Due Date

Peer Review Management

Grading Session

Tips for Best Results

Advanced Features

Automated Peer Review Follow-Up

Student Performance Analysis

Bulk Communication

Troubleshooting

"Connection failed" or "Authentication error"

"No students showing" or "empty results"

Anonymization Not Working

Need More Help?

Contributing

Support