Social MCP: Multi-Agent Social Media Automation
About
Social MCP is a multi‑agent system that automates content extraction, tweet generation, posting, and engagement on platforms like Twitter and Bluesky. It uses LLMs for content generation, Playwright for browser automation, and platform APIs for integration.
Details
- Author
- kitadmin01
- Downloads
- 189
- Categories
- AI
Jump to
- Persistent Twitter session management with automatic login detection
- Tweet posting with retry logic and navigation recovery
- LLM‑powered tweet generation and content scheduling
- Hashtag‑based content discovery and engagement automation
- Robust browser automation with page state verification
- Google Sheets integration and shared retry / secrets utilities
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Social MCP: Multi-Agent Social Media AutomationCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, create a Python virtual environment, install dependencies (including Playwright), and configure a .env file with Twitter credentials, Playwright session directory, and Google Sheets API / OAuth settings. Start the MCP server from /social_mcp with python mcp_server/server.py, then run the MCP client with python mcp_client/client.py.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"social mcp: multi-agent social media automation": {
"social_mcp": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"social_mcp": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}Social MCP: Multi-Agent Social Media Automation
Overview
Social MCP is a multi-agent system for automating content extraction, tweet generation, posting, and engagement on platforms like Twitter and Bluesky. It uses LLMs for content generation, Playwright for browser automation, and APIs for platform integration.Architecture
- MCP Server: Hosts tool endpoints for: - Content extraction - Tweet generation using LLMs - Browser automation for Twitter (using Playwright) - Social media engagement - Content scheduling - MCP Client: Orchestrates workflow, runs agents, manages LLM, and coordinates tool calls - Common: Shared utilities for Google Sheets integration, retry logic, and secrets managementCore Features
1. Twitter Automation - Persistent session management - Robust login detection - Tweet posting with retry logic - Search and engagement automation - Hashtag-based content discovery2. Content Generation
- LLM-powered tweet generation
- Content scheduling
- Multi-platform support
3. Browser Automation
- Persistent session handling
- Robust page state verification
- Automatic recovery from navigation issues
- URL encoding and proper page loading
Directory Structure
social_mcp/
├── mcp_server/
│ ├── tools/
│ │ ├── post_tweets.py # Twitter automation
│ │ ├── generate_tweets.py # LLM tweet generation
│ │ └── engage_posts.py # Social engagement
│ ├── server.py
│ └── config.py
├── mcp_client/
│ ├── agents/
│ ├── llm_orchestrator.py
│ ├── workflow_graph.py
│ └── client.py
├── common/
│ ├── google_sheets.py
│ ├── retry_utils.py
│ └── secrets.py
├── .env
├── requirements.txt
├── README.md
└── setup.sh
Implementation Details
Twitter Automation (post_tweets.py)
- Session Management - Persistent browser sessions - Automatic login detection - Session recovery- Robust Page Handling
- URL encoding for search terms
- Continuous page state verification
- Automatic navigation recovery
- Retry logic for failed operations
- Engagement Features
- Hashtag-based search
- Tweet liking automation
- Content discovery
Browser Automation Best Practices
1. Page Loading - Usedomcontentloaded for initial load
- Wait for specific elements
- Verify page state
2. Error Handling
- Retry logic for failed operations
- Graceful recovery from errors
- Detailed logging
3. Session Management
- Persistent context
- Login state verification
- Automatic session recovery
Setup
1. Clone the repo and enter the directory: git clone <repository-url>
cd social_mcp
2. Create a virtual environment and install dependencies:
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
playwright install
3. Configure your .env file with:
TWITTER_USERNAME=your_username
TWITTER_PASSWORD=your_password
PLAYWRIGHT_SESSION_DIR=./playwright_session
HEADLESS=false # Set to true for headless operation
4. Set up Google Sheets API and OAuth credentials
Usage
1. Start the MCP server from /social_mcp: python mcp_server/server.py
2. Run the MCP client from /social_mcp:
python mcp_client/client.py
Adding New Features
- New Tools: Add tomcp_server/tools/ and register in server.py
- New Agents: Add to mcp_client/agents/ and update workflow_graph.py
- Browser Automation: Follow the patterns in post_tweets.py for robust implementation
Security
- Store all secrets in.env
- Use OAuth scopes for Google Sheets and Bluesky
- Playwright scripts handle MFA/CAPTCHA gracefully
- Session data stored securely in playwright_session directory
Best Practices
1. Browser Automation - Always verify page state - Use proper URL encoding - Implement retry logic - Handle navigation issues2. Error Handling
- Log all operations
- Implement graceful recovery
- Use appropriate timeouts
3. Session Management
- Verify login state
- Handle session recovery
- Clean up resources properly
** To do
1. Get content from tweet and bsky, use LLM to get response and post it
2. Incorporate second twitter account to post and like
3. For search and engagement include searches "web3", "nft", 'crypto"
4. Timing the workflow starts and runs make it random from .env
5. Implement Reddit posting
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
