MCP Academic Structure Server

A powerful Model Context Protocol (MCP) server that provides intelligent access to your Google Drive-based academic structure. This server allows AI assistants like Claude to navigate, search, and analyze your academic materials organized in cycles, semesters, and modules.

Overview

The MCP Academic Structure Server transforms your Google Drive academic folders into an intelligent, searchable knowledge base that AI assistants can interact with. It provides:

• Hierarchical Navigation: Browse through academic cycles, semesters, and modules
• Intelligent Search: Find specific content across your entire academic structure
• Path Validation: Verify and navigate to specific academic paths
• Module Comparison: Compare different modules to understand relationships
• Subject Discovery: Find all modules related to specific subjects

Project Structure

MCP-ENSIA/
├── src/
│   ├── clients/
│   │   └── drive_client.py          # Enhanced Google Drive API client
│   ├── core/
│   │   ├── academic_structure.py    # Main academic processor
│   │   ├── cycle_manager.py         # Cycle processing logic
│   │   ├── semester_manager.py      # Semester processing logic
│   │   ├── module_manager.py        # Module processing logic
│   │   └── category_manager.py      # Category processing logic
│   ├── models/
│   │   ├── academic_models.py       # Data models for academic structure
│   │   └── search_models.py         # Search-related models
│   ├── services/
│   │   ├── search_service.py        # Search functionality
│   │   ├── navigation_service.py    # Navigation services
│   │   └── comparison_service.py    # Module comparison services
│   ├── utils/
│   │   └── validation_utils.py      # Validation utilities
│   ├── server.py                    # Main MCP server
├── config/
│   ├── .env                        # Environment variables
│   ├── credentials.json            # Google Drive credentials (user-specific)
│   └── token.json                  # OAuth token (auto-generated)
├── tests/                          # Comprehensive test suite
├── requirements.txt                # Python dependencies
└── README.md                       # This file

MCP Tools Available

Tool	Description	Parameters
get_academic_overview	Complete overview with statistics	max_depth (optional)
search_academic_content	Search across academic structure	query, scope, filters
navigate_to_path	Navigate to specific academic path	path
list_cycles	List all academic cycles	None
list_semesters	List semesters in a cycle	cycle
list_modules	List modules in a semester	cycle, semester
compare_modules	Compare two modules	module1_path, module2_path
find_modules_by_subject	Find modules by subject	subject, exact_match
get_structure_overview	Detailed structure information	None
validate_path	Validate academic path format	path

Prerequisites

• Python 3.8+
• Google Drive API access: Access is exclusive for now. Help is welcomed in hosting this server for public access.
• MCP-compatible AI assistant (like Claude Desktop)
• Academic structure in Google Drive following the expected format

Installation & Setup

1. Clone the Repository

git clone <repository-url>
cd MCP-ENSIA

2. Install Dependencies

pip install -r requirements.txt

3. Test the Setup

# Test imports and basic functionality
python -c "
import sys
sys.path.append('src')
from src.clients.drive_client import EnhancedGoogleDriveClient
from src.core.academic_structure import AcademicStructureProcessor
print('All imports successful!')
"

Usage

Running the MCP Server

# Start the MCP server
python -m src.server

Integration with Claude Desktop

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "academic-structure": {
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/MCP-ENSIA",
      "env": {
        "PYTHONPATH": "/path/to/MCP-ENSIA"
      }
    }
  }
}

Example Commands in Claude

Once integrated, you can ask Claude:

"Get an overview of my academic structure"
"List all my academic cycles" 
"Show me modules in First Superior Cycle, Semester 1"
"Search for automata theory content"
"Navigate to 3. First Superior Cycle/Semester 1/Theory of Computation"
"Compare Mathematics I from different semesters"
"Find all modules that teach machine learning"

Testing

Run Test Suite

# Install test dependencies
pip install pytest pytest-asyncio pytest-mock pytest-cov

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=src --cov-report=html

# Run specific test categories
pytest tests/test_imports.py -v           # Import tests
pytest tests/unit/ -v                     # Unit tests
pytest tests/integration/ -v              # Integration tests

License

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

Acknowledgments

This project was made possible thanks to ETC club for the Google Drive content that they collected under Ensia Hub.

Related Links

• Model Context Protocol Documentation
• Google Drive API Documentation
• Claude Desktop MCP Setup

Support

For questions, issues, or contributions:

1. Check existing issues in the repository
2. Create a new issue with detailed description
3. Join discussions in repository discussions
4. Contact maintainers via repository contacts

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Made with ❤️ for the academic community