🐦 X-Post MCP 🚀

🐦 X-Post MCP 🚀

A powerful integration that allows AI models to post directly to X (formerly Twitter) using the Model Context Protocol (MCP) 🤖✨

📋 Overview

X-Post MCP is a client-server application that enables AI models to create and publish posts on X (formerly Twitter) through a standardized interface. It leverages the Model Context Protocol to provide a seamless integration between AI models and the X platform.

✨ Features

• 🔄 Seamless integration with X (Twitter) API
• 🧠 AI-powered post creation using Google's Gemini models
• 🛠️ MCP server exposing X posting functionality as a tool
• 💬 Interactive chat interface for testing and demonstration
• 🔒 Secure handling of API credentials
• ✂️ Automatic truncation of posts exceeding X's character limit

🛠️ Technologies

• Bun: Fast all-in-one JavaScript runtime
• TypeScript: Type-safe JavaScript
• Express: Web framework for Node.js
• Model Context Protocol (MCP): Standardized protocol for AI context
• Twitter API v2: X platform API
• Google Gemini: Google's generative AI models

📦 Installation

Prerequisites

• Bun v1.2.5 or later
• X (Twitter) API credentials
• Google Gemini API key

Server Setup

# Clone the repository
git clone https://github.com/subhadeeproy3902/x-post-mcp.git
cd x-post-mcp

# Install server dependencies
cd server
bun install

Client Setup

# From the project root
cd client
bun install

⚙️ Configuration

Obtaining X (Twitter) API Credentials

1. Create a Developer Account:

   • Go to the X Developer Portal
   • Sign in with your X account or create a new one
   • Apply for a developer account if you don't have one already

2. Create a Project & App:

   • Once approved, create a new Project in the Developer Portal
   • Within the project, create a new App
   • Give your app a name and description

3. Set App Permissions:

   • In your App settings, navigate to the "User authentication settings" section
   • Edit the settings and enable OAuth 1.0a
   • Set App permissions to Read and Write and Direct Messages
   • Save your changes

4. Generate Access Tokens:

   • Navigate to the "Keys and Tokens" tab
   • Generate "Consumer Keys" (API Key and Secret)
   • Generate "Access Token and Secret" for your account
   • Make sure to save all four values as they will be needed for configuration

⚠️ Important: Keep your API keys and tokens secure and never commit them to public repositories.

Server Environment Variables

Create a .env file in the server directory with the following variables:

# X (Twitter) API Credentials
TWITTER_API_KEY=your_twitter_api_key
TWITTER_API_SECRET=your_twitter_api_secret
TWITTER_ACCESS_TOKEN=your_twitter_access_token
TWITTER_ACCESS_SECRET=your_twitter_access_secret

Obtaining Google Gemini API Key

1. Create a Google AI Studio Account:

   • Go to Google AI Studio
   • Sign in with your Google account

2. Get API Key:

   • Navigate to the API Keys section
   • Click "Create API Key"
   • Copy your new API key

⚠️ Important: Keep your API key secure and never commit it to public repositories.

Client Environment Variables

Create a .env file in the client directory with the following variables:

# Google Gemini API Key
GEMINI_API_KEY=your_gemini_api_key

🚀 Usage

Starting the Server

cd server
bun run dev

The server will start on http://localhost:3001.

Starting the Client

cd client
bun run index.ts

The client will connect to the server and provide an interactive chat interface where you can interact with the AI model and instruct it to post to X.

Example Commands

Once the client is running, you can interact with the AI model:

You: Post a tweet about the beautiful weather today

The AI will use the Gemini model to generate a post and then use the MCP tool to publish it to X.

🏗️ Architecture

Server

The server component is built with Express and implements the Model Context Protocol. It exposes a tool called createPost that can be used to post to X. The server uses Server-Sent Events (SSE) for communication with clients.

Client

The client component connects to the MCP server and provides an interface for interacting with Google's Gemini AI models. It translates user requests into AI-generated content and can call the server's tools to post to X.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

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

🙏 Acknowledgements

• Model Context Protocol for providing the standardized AI context protocol
• Twitter API v2 for X platform integration
• Google Gemini for AI capabilities