ExamPrepAgent

cardea-mcp/ExamPrepAgent

3.3

If you are the rightful owner of ExamPrepAgent and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

The Model Context Protocol (MCP) server is a framework designed to facilitate the interaction between large language models (LLMs) and a knowledge base, enabling efficient search and retrieval of information for educational purposes.

Tools
2
Resources
0
Prompts
0

🧠 ExamPrepAgent

An AI agent that assists students preparing for any exam or test, such as Linux Foundation (LF) certifications. The agent serves as an intelligent study companion leveraging Large Language Models (LLMs) and MCP servers to provide an engaging and interactive learning experience. The agent supports:

  • Ask random study questions
  • Semantic and keyword search of the correct answers
  • Guided conversation for deeper understanding

It consists of two major components.

  • An MCP (Model Context Protocol) server that provides tools to search for study questions and answers from a knowledge base. It can be used with any MCP-compatible LLM client.
  • A chatbot application that utilizes any LLM and the MCP server. It asks the user study questions, and helps the user to reach the correct answer.

Live demo: http://k8s.cardea.cc/

Video demo: https://x.com/realwasmedge/status/1964435649534316809

🚀 Getting Started

  1. Clone the repository.
https://github.com/cardea-mcp/ExamPrepAgent.git
  1. Install dependencies
pip install fastmcp fastapi requests mysql-connector-python ffmpeg
  1. Create a .env file from the .env.example file and fill in the required values.

You will need API endpoints for an LLM service, and the system prompt for the conversation management. If you want to use the voice features on the chatbot app, you will also need API endpoints for ASR and TTS services. You can run LLM, ASR, and TTS services using open-source models locally on your own computers via LlamaEdge.

  1. Set up the question and answer database.

First download a CSV file for the QA knowledge base. The CSV file contains 3 columns: the question, the answer, and an explanation of the answer. The CSV file must be saved as dataset/qa.csv.

cd dataset
curl -L -o qa.csv https://huggingface.co/datasets/ItshMoh/k8_qa_pairs/resolve/main/kubernetes_qa_output.csv

Run the script to load the CSV file into a database. The database connection URL and table name are configured in the .env file.

python csv_loader.py
  1. Start the MCP server.
python3 main.py
  1. Start the chatbot app.
python3 app.py

ScreenShots of the bot in action

We have created two datasets for the QA knowledge base for you to experiment with. The Kubernetes QA is the default dataset used in our scripts.

🏗️ Architecture

🔧 Key Files Description

Core Application Files

app.py

app.py: Direct API integration architecture

  • Communicates directly with LLM APIs (OpenAI-compatible)
  • Manages MCP server subprocess internally
  • Suitable for direct API deployments
LLM Integration Files

llm_api.py - FastAPI Integration (Direct)

  • HTTP API endpoint integration
  • Subprocess MCP server management
  • Audio message processing
  • Session-based context handling
MCP & Database

main.py - MCP Server

  • Implements FastMCP server
  • Provides get_random_question() and get_question_and_answer() tools
  • Interfaces with TiDB for full-text search The system is composed of several core components:

database/tidb.py - TiDB Integration

  • Vector similarity search
  • Full-text search capabilities
  • Q&A pair management
  • Bulk data operations

✨ Features

Core Workflows

📌 Workflow 1: Question Search
  1. User asks a specific question.
  2. LLM invokes the get_question_and_answer() MCP function.
  3. The system searches the TiDB for relevant Q&A pairs.
  4. The LLM provides a contextual, helpful response based on the findings.
🎯 Workflow 2: Practice Mode
  1. User requests a practice question.
  2. LLM invokes the get_random_question() MCP function.
  3. The system returns a random Q&A pair.
  4. The LLM presents the question and guides the user's learning.

🛠️ Technical Stack

  • Python 3.x
  • FastMCP – MCP server framework
  • TiDB – SQL database for Full text Search
  • JSON – Data storage format

📁 Project Structure

└── ExamPrepAgent/
    ├── README.md
    ├── app.py
    ├── llm_api.py
    ├── llmclient.py
    ├── load_dataset.py
    ├── main.py
    ├── requirements.txt
    ├── setup_dataset.sh
    ├── .env.example
    ├── audio_processing/
    │   ├── audio_utils.py
    │   ├── tts_handler.py
    │   └── whisper_handler.py
    ├── database/
    │   ├── dataloader.py
    │   └── tidb.py
    ├── dataset/
    │   ├── csv_loader.py
    │   └── dataPrep.py
    ├── static/
    │   ├── audio_recorder.js
    │   ├── index.html
    │   ├── script.js
    │   ├── styles.css
    │   └── uploads/
    │       └── .gitkeep
    └── utils/
        └── ques_select.py

🔮 Future Enhancements

  • Expanded Datasets: Add more LF certification topics
  • Advanced Analytics: Track learning progress and weak areas
  • Multi-modal Support: Include diagrams and visual aids

Kubernetes Dataset

The dataset has been created using the kubernetes.io documentation. It is available under CC BY 4.0. license.

Here is the link to the dataset

The dataset has been generated using this script

🤝 Contributing

  1. Fork the repository
  2. Create your feature branch: 
    git checkout -b feature/amazing-feature
  3. Add your Q&A datasets in the specified format
  4. Test your changes with the MCP server
  5. Submit a pull request.