README.md

---
title: Model Context Protocol (MCP) example
emoji: 📋
colorFrom: blue
colorTo: purple
sdk: docker
sdk_version: "3.11"
app_file: mcp_task_client.py
pinned: false
license: mit
short_description: MCP Task Manager CLI
---

# Model Context Protocol (MCP) Example Implementation

This repository demonstrates the implementation of the Model Context Protocol (MCP), a standardized approach for managing context in AI/ML models.

To get more details, look at the post [How to create an MCP server and client with an LLM](https://jstoppa.com/posts/how-to-create-a-model-context-protocol-mcp-to-give-context-to-an-llm/post/)

## Overview

MCP (Model Context Protocol) is a protocol designed to standardize how context is handled when working with AI/ML models. This example repository shows how to implement and use MCP in your projects.

## Getting Started

### Prerequisites

- Python 3.8 or higher
- pip (Python package installer)

### Setup Virtual Environment

#### Windows
First, open Command Prompt and follow these steps:

1. Create and navigate to your project directory:
```bash
cd mcp_example
```

2. Create a virtual environment:
```bash
python -m venv app
```

3. Activate the virtual environment:
```bash
venv\Scripts\activate
```

4. Your prompt should now show "(venv)" at the beginning, indicating the virtual environment is active.

5. Install required packages:
```bash
pip install -r requirements.txt
```

#### macOS
Open Terminal and follow these steps:

1. Create and navigate to your project directory:
```bash
mkdir mcp_example
cd mcp_example
```

2. Create a virtual environment:
```bash
python3 -m venv venv
```

3. Activate the virtual environment:
```bash
source venv/bin/activate
```

4. Your prompt should now show "(venv)" at the beginning, indicating the virtual environment is active.

5. Install required packages:
```bash
pip install -r requirements.txt
```