🎓 MCP Student Assistant

An AI-powered Student Assistant built with the Model Context Protocol (MCP). It exposes a set of student-focused tools — attendance lookup, marks calculator, notes search, college rules, and a math calculator — through an MCP server, with a companion CLI client powered by Groq's Llama 3.3 70B for natural-language interaction.

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✨ Features

Tool	Description
Calculator	Evaluate arbitrary math expressions (e.g. 45*12+5)
Attendance	Look up a student's attendance record from attendance.csv
Marks	Compute a student's average across Math, Physics, Chemistry, Java & Python
Notes Search	Search for keywords inside notes.pdf
College Rules	Display all college rules from rules.txt

For questions that don't match any tool, the client falls back to Groq's Llama 3.3 70B model for general-purpose answers.

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📁 Project Structure

MCP/
├── server.py          # MCP server — registers all tools
├── client.py          # CLI client — keyword routing + Groq fallback
├── attendance.py      # Attendance lookup (pandas + CSV)
├── marks.py           # Average marks calculator (pandas + CSV)
├── notes.py           # Keyword search across PDF notes
├── pdf_reader.py      # PDF text extraction (pypdf)
├── calculator.py      # Simple eval-based calculator
├── rules.py           # Reads college rules from a text file
├── requirements.txt   # Python dependencies
├── .env               # Environment variables (GROQ_API_KEY)
└── data/
    ├── attendance.csv  # Student attendance records
    ├── marks.csv       # Student marks (5 subjects)
    ├── notes.pdf       # Course notes (PDF)
    ├── notes.txt       # Course notes (plain text)
    ├── rules.txt       # College rules
    └── students.csv    # Student roster

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🚀 Getting Started

Prerequisites

• Python 3.10+
• A Groq API key (free tier available)

1. Clone the repository

git clone <repo-url>
cd MCP

2. Create a virtual environment (recommended)

python -m venv venv
# Windows
venv\Scripts\activate
# macOS / Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

Create a .env file in the project root (one is already included):

GROQ_API_KEY=your_groq_api_key_here

5. Run the application

Option A — MCP Server (exposes tools over the MCP protocol):

python server.py

Option B — CLI Client (interactive chat with keyword routing + Groq fallback):

python client.py

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💬 Usage Examples

Ask : What is Rahul's attendance?
→ {'Name': 'Rahul', 'Days_Present': 42, 'Total_Days': 50, ...}

Ask : Show me Priya's average marks
→ Average = 82.4

Ask : What are the college rules?
→ (displays all rules from rules.txt)

Ask : Search notes for machine learning
Keyword : machine learning
→ (matching lines from notes.pdf)

Ask : Calculate 25+50*2
→ 125

Ask : What is the capital of France?
→ (answered by Groq Llama 3.3 70B)

Ask : exit
→ (exits the program)

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🛠️ Tech Stack

• MCP (Model Context Protocol) — tool registration & server
• Groq — LLM inference (Llama 3.3 70B)
• pandas — CSV data processing
• pypdf — PDF text extraction
• python-dotenv — environment variable management

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📝 License

This project is for educational purposes.