How do I use Educational Tutor MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Educational Tutor MCP Server generate a beginner course on Python from the official documentation" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Educational Tutor

An experimental system that transforms documentation repositories into interactive educational content using AI and the Model Context Protocol (MCP).

🌟 Overview

This project consists of two main components:

📚 Course Content Agent - Generates structured learning courses from documentation repositories
🔧 MCP Educational Server - Provides standardized access to course content via MCP protocol

🏗️ Architecture

Documentation Repository → Course Content Agent → Structured Courses → MCP Server → AI Tutors

The system processes documentation, creates educational content, and exposes it through standardized tools for AI tutoring applications.

📂 Project Structure

tutor/ ├── course_content_agent/ # AI-powered course generation from docs │ ├── main.py # CourseBuilder orchestration │ ├── modules.py # Core processing logic │ ├── models.py # Pydantic data models │ ├── signatures.py # DSPy LLM signatures │ └── about.md # 📖 Detailed documentation ├── mcp_server/ # MCP protocol server for course access │ ├── main.py # MCP server startup │ ├── tools.py # Course interaction tools │ ├── course_management.py # Content processing │ └── about.md # 📖 Detailed documentation ├── course_output/ # Generated course content ├── nbs/ # Jupyter notebooks for development └── pyproject.toml # Project configuration

🚀 Quick Start

1. Install Dependencies and Create Virtual Environment

This project uses uv for fast Python package management.

# Create a virtual environment python -m uv venv # Install dependencies in editable mode .venv/bin/uv pip install -e .

2. Generate Courses from Documentation

# Generate courses from a repository .venv/bin/uv run python course_content_agent/test.py

Customize for Your Repository: Edit course_content_agent/test.py to change:

Repository URL (currently uses MCP docs)
Include/exclude specific folders
Output directory and caching settings

3. Start MCP Server

# Serve generated courses via MCP protocol .venv/bin/uv run python -m mcp_server.main # Or customize course directory COURSE_DIR=your_course_output .venv/bin/uv run python -m mcp_server.main

4. Test MCP Integration

# Test server capabilities .venv/bin/uv run python mcp_server/stdio_client.py

📖 Detailed Documentation

For comprehensive information about each component:

Course Content Agent: See course_content_agent/about.md
- AI-powered course generation
- DSPy signatures and multiprocessing
- Document analysis and learning path creation
MCP Educational Server: See mcp_server/about.md
- MCP protocol implementation
- Course interaction tools
- Integration with AI assistants

🔌 MCP Integration with Cursor

To use the educational tutor MCP server with Cursor, create a .cursor/mcp.json file in your project root:

{ "mcpServers": { "educational-tutor": { "command": "/path/to/tutor/project/.venv/bin/uv", "args": [ "--directory", "/path/to/tutor/project", "run", "mcp_server/main.py" ], "env": { "COURSE_DIR": "/path/to/tutor/project/course_output" } } } }

Setup Steps:

Create a virtual environment: python -m uv venv
Install dependencies: .venv/bin/uv pip install -e .
Update the command path and the path in args to your project directory.
Restart Cursor or reload the window.
Use @educational-tutor in Cursor chat to access course tools.

🔧 Development Status

Current Status: ✅ Functional MVP

Course generation from documentation repositories
MCP server for standardized content access
Multi-complexity course creation (beginner/intermediate/advanced)

Future Enhancements:

Support for diverse content sources (websites, videos)
Advanced search and recommendation systems
Integration with popular AI platforms

🛠️ Technology Stack

AI Framework: DSPy for LLM orchestration
Content Processing: Multiprocessing for performance
Protocol: Model Context Protocol (MCP) for standardization
Models: Gemini 2.5 Flash for content generation
Data: Pydantic models for type safety

📄 License

This project is experimental and intended for educational and research purposes.

Educational Tutor MCP Server