Provides database support for storing post schedules and content, serving as a backend for the LinkedIn post automation system.
Provides the runtime environment for the MCP server, supporting the LinkedIn post automation functionality through its ecosystem.
Offers database functionality for storing post schedules and content as an alternative to PostgreSQL, enabling local data persistence for the LinkedIn automation service.
LinkedIn Automated Post Creator
This project automates the creation of LinkedIn posts using MCP server integration. It allows users to schedule and create posts automatically on their LinkedIn account.
Architecture
The project follows a modular architecture with the following components:
MCP Server: Handles message control and scheduling
LinkedIn API Integration: Manages LinkedIn authentication and post creation
Scheduler: Manages post scheduling and timing
Content Generator: Generates or manages post content
Database: Stores post schedules and content
Related MCP server: YouTube to LinkedIn MCP Server
Project Structure
Setup Instructions
Install dependencies:
Configure LinkedIn API credentials in config/config.py
Run the MCP server:
Features
Automated LinkedIn post creation
Customizable post scheduling
Content management
MCP server integration
Real-time post monitoring
Requirements
Python 3.8+
LinkedIn API credentials
MCP server access
Database (SQLite/PostgreSQL)
Deployment
The project can be deployed on any server with Python support. Follow the deployment guide in docs/deployment.md for detailed instructions.