MCP Knowledge Base Assistant

A demonstration of the Model Context Protocol (MCP) that connects an OpenAI-powered client to a knowledge base server. This project showcases how to build a simple but powerful AI assistant that can answer questions about company policies by accessing a knowledge base through MCP.

📋 Overview

This project demonstrates:

1. How to build an MCP server that exposes a knowledge base as a tool
2. How to create an MCP client that connects to the server
3. How to integrate OpenAI's API to create a natural language interface
4. How to use Docker to containerize the server component

The system allows users to ask questions in natural language about company policies, and the AI will retrieve relevant information from the knowledge base to provide accurate answers.

🏗️ Architecture

The project follows the MCP client-host-server architecture:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │
│  OpenAI Model   │◄────┤  MCP Client     │◄────┤  MCP Server     │
│  (GPT-4o)       │     │  (client.py)    │     │  (server.py)    │
│                 │     │                 │     │                 │
└─────────────────┘     └─────────────────┘     └─────────────────┘
                                                        │
                                                        ▼
                                               ┌─────────────────┐
                                               │                 │
                                               │  Knowledge Base │
                                               │  (kb.json)      │
                                               │                 │
                                               └─────────────────┘

- MCP Server: Exposes the knowledge base as a tool that can be queried
- MCP Client: Connects to the server and integrates with OpenAI's API
- OpenAI Model: Processes natural language queries and generates responses
- Knowledge Base: JSON file containing Q&A pairs about company policies

🚀 Getting Started

Prerequisites

- Python 3.11 or higher
- Docker (optional, for containerized server)
- OpenAI API key

Installation

1. Clone the repository:

   git clone <repository-url>

   cd MCP-Get-Started

   

2. Create a virtual environment and install dependencies:

   python -m venv venv

   

   # On Windows

   venv\Scripts\activate

   

   # On macOS/Linux

   source venv/bin/activate

   

   pip install -r requirements.txt

   

3. Create a .env file in the project root with your OpenAI API key:

   OPENAI_API_KEY=your_openai_api_key_here

   

Running the Server

Option 1: Run directly with Python

python server.py

Option 2: Run with Docker

```