Sarthak-Neupane/CanvasMCPServer

Canvas MCP Server

A Model Context Protocol (MCP) server that provides Canvas-related tools for AI assistants.

Features

• 🎨 Canvas tool integration
• 🔧 Extensible tool architecture
• 🚀 Fast and lightweight

Installation

From Source

git clone https://github.com/sarthakneupane/canvas-mcp-server.git
cd canvas-mcp-server
pip install -e .

Environment Setup

1. Copy the example environment file:

   cp .env.example .env
   

2. Edit .env and add your API credentials:

   CANVAS_API_TOKEN=your_actual_api_token_here
   CANVAS_BASE_URL=https://your-api.example.com
   

Development Installation

git clone https://github.com/sarthakneupane/canvas-mcp-server.git
cd canvas-mcp-server
pip install -e ".[dev]"

Usage

As a Standalone Server

canvas-mcp-server

With Claude Desktop

Add to your Claude Desktop configuration: (You might need to add full path to the directory)

{
  "mcpServers": {
    "canvas-mcp-server": {
      "command": "canvas-mcp-server"
    }
  }
}

Programmatic Usage

from canvas_mcp_server import main

# Run the server
main()

Available Tools

Canvas Course Management

get_all_courses:

Retrieve all accessible courses with pagination support Filter by enrollment state, type, and workflow state Include additional fields: syllabus, teachers, sections, student counts, etc. Smart pagination (return all or limit results)

get_course_by_id:

Get detailed information about a specific course Access course metadata, permissions, and detailed content Include teachers, sections, progress tracking, and more

Development

Running Tests

pytest

Code Formatting

black src/ tests/
isort src/ tests/

Type Checking

mypy src/ --strict

Project Structure

canvas-mcp-server/
├── src/canvas_mcp_server/    # Main package
│   ├── server.py             # Server implementation
│   └── tools/                # Available tools
├── tests/                    # Test suite
├── examples/                 # Usage examples
└── scripts/                  # Development scripts

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-feature)
3. Make your changes
4. Run tests (pytest)
5. Commit your changes (git commit -m 'Add new feature')
6. Push to the branch (git push origin feature/new-feature)
7. Open a Pull Request

License

This project is licensed under the MIT License - see the file for details.

Author

Sarthak Neupane

• GitHub: @Sarthak-Neupane