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Study Tools MCP

francis-rf/study-Tools-mcp-server
0 starsUpdated 2026-03-28Community

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Works with

Claude CodeClaude DesktopCursorVS CodeClineCodex CLIOpenClaw+ any MCP client

Install to Claude Code

This server doesn't publish a one-line install command. Follow the setup in the source repository.

Summary

An AI-powered study assistant that generates quizzes, flashcards, summaries, and concept explanations from study materials using the Model Context Protocol.

README.md

Study Tools MCP 📚

!Python !FastAPI !MCP !License ![CI/CD](https://github.com/francis-rf/study-Tools-mcp-server/actions/workflows/deploy.yml) ![Live Demo](http://3.210.199.248:8080/)

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

🛠️ 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
  1. Install dependencies:
pip install -r requirements.txt
  1. Create .env file:
cp .env.example .env
# Edit .env and add your OPENAI_API_KEY
  1. Add study materials:

Place PDF or Markdown files in data/notes/: `` data/notes/ ├── Machine Learning.pdf └── Your Notes.md ``

  1. Run the application:
python app.py
  1. 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

| Service | Purpose | |---------|---------| | EC2 (t2.micro) | Hosts the Docker container | | S3 (study-tools-mcp-materials) | Stores PDF study materials | | Secrets Manager (study-tools-mcp) | Stores OpenAI API key | | IAM Role | Grants EC2 access to S3 and Secrets Manager |

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

| Secret | Description | |--------|-------------| | EC2_HOST | EC2 instance public IP | | EC2_USER | ubuntu | | EC2_SSH_KEY | Contents of the .pem key file |

Workflow Status

![Deploy to AWS EC2](https://github.com/francis-rf/study-Tools-mcp-server/actions/workflows/deploy.yml)

📁 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

| Method | Endpoint | Description | |--------|----------|-------------| | GET | / | Web UI | | GET | /health | Health check | | GET | /api/files | List available study materials | | POST | /api/chat | Chat with streaming | | POST | /api/chat/clear | Clear conversation history |

🔌 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

!Application Interface _Study Tool AI Interface with quiz generation_

!Claude Desktop Integration _Study Tool AI Integration with Claude Desktop_

📄 License

MIT License

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