Study Tools MCP 📚

An AI-powered study assistant built with Model Context Protocol (MCP) that generates quizzes, flashcards, summaries, and concept explanations from your study materials.

🎯 Features

• Smart Summarization — Generate concise summaries from study materials

• Quiz Generation — Create customizable quizzes with difficulty levels

• Concept Explanation — Get beginner/intermediate/advanced explanations

• Flashcards — Auto-generate flashcard decks from documents

• Comparison Tool — Compare and contrast multiple concepts

• MCP Integration — Works directly with Claude Desktop

• Web UI — Standalone chat interface with FastAPI backend

Related MCP server: Interleaved Learning MCP Server

🛠️ Tech Stack

• Backend: FastAPI + Python 3.10

• AI Framework: Model Context Protocol (MCP)

• AI: OpenAI API

• Document Parsing: PyPDF2, pdfplumber, python-docx

• Frontend: Vanilla JavaScript, HTML, CSS

• Cloud: AWS EC2 + S3 + Secrets Manager

• CI/CD: GitHub Actions

🚀 Quick Start

Prerequisites

• Python 3.10+

• OpenAI API key

Installation

1. Clone the repository:

git clone https://github.com/francis-rf/study-Tools-mcp-server.git
cd study-Tools-mcp-server

2. Install dependencies:

pip install -r requirements.txt

3. Create .env file:

cp .env.example .env
# Edit .env and add your OPENAI_API_KEY

4. Add study materials:

Place PDF or Markdown files in data/notes/:

data/notes/
├── Machine Learning.pdf
└── Your Notes.md

5. Run the application:

python app.py

6. Open browser:

http://localhost:8080

🐳 Docker Deployment

Build and Run

docker build -t study-tools-mcp .
docker run -p 8080:8080 --env-file .env study-tools-mcp

☁️ AWS Deployment

Services Used

Setup

1. Store OpenAI API key in AWS Secrets Manager under secret name study-tools-mcp

2. Upload PDFs to S3 bucket study-tools-mcp-materials

3. Launch EC2 instance with IAM role attached (study-tools-mcp-ec2-role)

4. SSH in, install Docker, clone repo and run container

⚙️ GitHub Actions CI/CD

Automated deployment is configured via .github/workflows/deploy.yml.

Workflow: Deploy to AWS EC2

On every push to main, the pipeline:

1. Checks out the code

2. SSHs into the EC2 instance

3. Pulls latest code from GitHub

4. Rebuilds the Docker image

5. Restarts the container with zero downtime

Required GitHub Secrets

Workflow Status

📁 Project Structure

study-Tools-mcp-server/
├── app.py                          # FastAPI web application
├── src/study_tools_mcp/
│   ├── server.py                   # MCP server entry point
│   ├── config.py                   # Configuration (Secrets Manager + .env fallback)
│   ├── tools/                      # Quiz, flashcards, summarizer, explainer
│   ├── parsers/                    # PDF and Markdown parsers
│   └── utils/                      # Logger
├── static/                         # Frontend assets
├── templates/                      # HTML templates
├── data/notes/                     # Study materials (local only — S3 on AWS)
├── logs/                           # Application logs
├── .github/workflows/              # CI/CD
│   └── deploy.yml
├── Dockerfile
├── requirements.txt
└── pyproject.toml

📡 API Endpoints

🔌 Claude Desktop Integration

Add to %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "study-tools-mcp": {
      "command": "uv",
      "args": ["--directory", "C:\\path\\to\\study-tools-mcp", "run", "study-tools-mcp"]
    }
  }
}

Restart Claude Desktop — the tools will be available automatically.

📸 Screenshots

Study Tool AI Interface with quiz generation

Study Tool AI Integration with Claude Desktop

📄 License

MIT License