Model Context Protocol (MCP) - Complete Implementation Guide

🚀 Overview

Model Context Protocol (MCP) is an open standard that enables seamless integration between AI applications and external data sources and tools. This guide provides a complete end-to-end implementation with all necessary configuration files and integration examples for ChatGPT, Claude, and other AI models.

📋 Table of Contents

🤔 What is MCP?

MCP (Model Context Protocol) is a standardized way to:

Connect AI models to external data sources
Provide tools and functions that AI models can use
Enable secure and controlled access to resources
Create reusable components across different AI applications

Key Components:

MCP Server: Provides tools, resources, and prompts
MCP Client: AI applications that consume MCP services
Transport Layer: Communication protocol (stdio, HTTP, WebSocket)

✨ Benefits

Standardized Integration: Universal protocol for AI model connections
Security: Controlled access to external resources
Reusability: One MCP server can serve multiple AI applications
Extensibility: Easy to add new tools and resources
Local Development: Run everything locally for privacy and control

🔧 Prerequisites

Required Software:

Node.js (v18 or later) or Python (3.8+)
Git
PowerShell (Windows)
VS Code (recommended)

For AI Model Integration:

API keys for your chosen AI models
Claude Desktop, ChatGPT Desktop, or compatible client

🚀 Quick Start

1. Clone and Setup

# Create project directory mkdir mcp-implementation cd mcp-implementation # Initialize the project git init npm init -y # or use Python if preferred

2. Install Dependencies

# For Node.js implementation npm install @modelcontextprotocol/sdk express cors dotenv # For Python implementation (alternative) pip install mcp python-dotenv fastapi uvicorn

3. Run the Example Server

# Start the MCP server node server.js # Or for Python python server.py

4. Configure Your AI Client

Update your AI client configuration (examples provided below for each platform).

🛠️ Server Implementation

Node.js MCP Server

Our MCP server will provide:

File system tools
Web scraping capabilities
System information
Custom business logic

See server.js for the complete implementation.

Python MCP Server (Alternative)

For Python developers, we also provide a Python implementation in server.py.

🤖 AI Model Integration

Claude Desktop Integration

Claude Desktop has native MCP support. Configuration is done through claude_desktop_config.json.

ChatGPT Integration

Integration through custom plugins or API wrapper. See chatgpt-integration/ directory.

Other AI Models

Generic HTTP client implementation for any AI model that supports external tool calling.

⚙️ Configuration Files

This repository includes configuration files for:

claude_desktop_config.json - Claude Desktop MCP configuration
chatgpt-config.json - ChatGPT plugin configuration
mcp-config.json - Generic MCP server configuration
.env - Environment variables and API keys
package.json - Node.js dependencies and scripts

🏠 Local Development

Development Scripts

We provide PowerShell scripts for easy development:

scripts/setup.ps1 - Initial setup and dependency installation
scripts/start-dev.ps1 - Start development server with hot reload
scripts/test.ps1 - Run tests and validation

Environment Setup

Copy .env.example to .env
Fill in your API keys and configuration
Run the setup script

.\scripts\setup.ps1

🚀 Deployment

Local Deployment

# Production build npm run build # Start production server npm start

Docker Deployment

# Build Docker image docker build -t mcp-server . # Run container docker run -p 3000:3000 --env-file .env mcp-server

Cloud Deployment

Instructions for deploying to:

Heroku
AWS Lambda
Google Cloud Functions
Azure Functions

🔧 Troubleshooting

Common Issues

Connection Refused: Check if MCP server is running
Authentication Errors: Verify API keys in .env
Tool Not Found: Ensure tools are properly registered
CORS Issues: Check CORS configuration in server

Debugging

# Enable debug logging $env:DEBUG = "mcp:*" node server.js

Health Check

# Test server health curl http://localhost:3000/health

🚀 Advanced Features

Custom Tools

Learn how to create custom tools for your specific use case.

Resource Management

Implement resource caching and management for better performance.

Security

Best practices for securing your MCP server and API keys.

Monitoring

Set up logging and monitoring for production deployments.

📁 Project Structure

mcp-implementation/ ├── README.md # This file ├── server.js # Main MCP server (Node.js) ├── server.py # Alternative Python server ├── package.json # Node.js dependencies ├── requirements.txt # Python dependencies ├── .env.example # Environment variables template ├── claude_desktop_config.json # Claude Desktop configuration ├── chatgpt-config.json # ChatGPT integration config ├── mcp-config.json # Generic MCP configuration ├── Dockerfile # Docker container configuration ├── scripts/ │ ├── setup.ps1 # Setup script for Windows │ ├── start-dev.ps1 # Development server script │ └── test.ps1 # Testing script ├── examples/ │ ├── claude-integration/ # Claude-specific examples │ ├── chatgpt-integration/ # ChatGPT integration examples │ └── generic-client/ # Generic client examples ├── tools/ │ ├── filesystem.js # File system tools │ ├── web-scraper.js # Web scraping tools │ └── system-info.js # System information tools └── tests/ ├── server.test.js # Server tests └── integration.test.js # Integration tests

📚 Next Steps

Follow the Quick Start guide
Explore the example implementations
Configure your preferred AI model
Customize tools for your use case
Deploy to your preferred platform

🤝 Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests for any improvements.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

If you encounter any issues:

Check the Troubleshooting section
Search existing GitHub issues
Create a new issue with detailed information

Happy coding with MCP! 🚀

Deploy Server

HTTP connection URL

security - not tested

license - not found

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

Provides a complete end-to-end MCP server implementation with file system tools, web scraping capabilities, and system information access. Includes ready-to-use configuration files and integration examples for Claude Desktop, ChatGPT, and other AI models.