What is Model Context Protocol 101

Model-Context-Protocol-101 is a comprehensive tutorial designed to guide AI/ML practitioners, consultants, and developers in understanding and implementing the Model Context Protocol (MCP). It provides structured learning through code examples, explanations, and exercises.

Use cases

Use cases include developing AI models that require context-aware processing, automating data extraction from CSV files, and enhancing interactive coding skills through hands-on exercises.

How to use

To use Model-Context-Protocol-101, clone the repository, create a Python virtual environment, install the required dependencies, and run Jupyter Notebook for interactive experimentation.

Key features

Key features include MCP server setup, function expansion for custom tools, CSV file handling for data extraction, and practical coding exercises to enhance understanding.

Where to use

Model-Context-Protocol-101 can be used in fields such as artificial intelligence, machine learning, data science, and software development, where understanding of context protocols is essential.

🚀 Model-Context-Protocol-101

A step-by-step tutorial exploring the Model Context Protocol (MCP). This repository serves as a structured learning guide for AI/ML practitioners, consultants, and developers interested in practical MCP implementation.

📌 Overview

This repository covers:
✔️ Setting up a Python virtual environment for isolated development.
✔️ Installing required dependencies using pip install -r requirements.txt.
✔️ Understanding MCP concepts with practical code examples.
✔️ Running Jupyter Notebooks for interactive experimentation.

🛠️ Getting Started

1️⃣ Clone the Repository

git clone https://github.com/itprodirect/Model-Context-Protocol-101.git
cd Model-Context-Protocol-101

2️⃣ Create a Virtual Environment

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

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run Jupyter Notebook

jupyter notebook notebooks/Model-Context-Protocol-101.ipynb

5️⃣ Run Tests

pytest

🔑 Key Features

• 🚀 MCP Server Setup: Learn how to initialize and expand an MCP tool.
• 🔧 Function Expansion: Add custom tools and test them interactively.
• 📂 CSV File Handling: Automate CSV file reading and data extraction.
• 🎯 Practical Exercises: Hands-on coding exercises for better understanding.

🌟 Real-world Use Cases

Here are quick examples of how an independent insurance agent might apply MCP:

• Automated Quotes – load policy data from a CSV and generate quotes in seconds.
• Lead Tracking – triage new leads automatically using simple prompts.
• Commission Insights – compute profits and commissions with a single command.

To explore these datasets yourself, open the notebook
notebooks/Model-Context-Protocol-101.ipynb.
It demonstrates how to load data/insurance_sales.csv and calculate totals.

You can also inspect the CSV quickly from the command line:

python - <<'EOF'
import pandas as pd
df = pd.read_csv('data/insurance_sales.csv')
print(df.head())
EOF

📖 Usage Guide

This tutorial walks through how to:
✅ Initialize the MCP Server
✅ Test MCP tools locally
✅ Expand MCP with custom functions
✅ Read and process CSV files
✅ Deploy and use MCP tools efficiently

🔄 Workflow

Use the command-line interface to run common tasks directly from the terminal.

# Calculate profit from revenue and cost
python src/cli.py profit 1000 600

# Total commission from the sample dataset
python src/cli.py commission data/insurance_sales.csv

📂 Project Structure

Model-Context-Protocol-101/
├── src/                # Python utilities
├── notebooks/          # Jupyter notebooks
├── data/               # Sample datasets
├── docs/img/           # Diagrams and screenshots
├── tests/              # Unit tests
├── README.md           # Documentation
├── requirements.txt    # Dependencies
├── LICENSE             # Project License
└── AGENTS.md           # Contribution guide

📚 Glossary

📝 License

This project is licensed under the MIT License.

🤝 Contributing

Contributions are welcome! Feel free to fork the repo, submit pull requests, or suggest improvements.

📬 Contact

For questions or collaborations, connect with me on LinkedIn or open an Issue in this repository.

❓ Common Issues

Virtual environment won’t activate
Make sure you run python -m venv venv and then activate it with
source venv/bin/activate on Mac/Linux or venv\Scripts\activate on Windows.
Verify Python 3.8+ is installed.

Missing packages
Run pip install -r requirements.txt from the project root while your virtual
environment is active. This installs all dependencies, including Jupyter.

Notebook won’t launch
Activate your virtual environment and run
jupyter notebook notebooks/Model-Context-Protocol-101.ipynb. If the command is
not found, install Jupyter using pip install jupyter.

🔥 This README is designed for clarity, readability, and ease of navigation! 🚀