🚀 Social Media Management Suite - MCP Server

A comprehensive Model Context Protocol (MCP) server that provides AI-powered social media management tools. This server enables automated content creation, scheduling, analytics, competitor analysis, and hashtag optimization across multiple platforms.

🌟 Features

📱 Core Social Media Tools

• Post Scheduling - Schedule posts across Twitter, Instagram, Facebook, LinkedIn
• Hashtag Generation - AI-powered hashtag suggestions with trending analysis
• Analytics & Insights - Comprehensive engagement metrics and audience analytics
• Trending Topics - Real-time trend tracking with content ideas
• Content Management - View and manage scheduled posts

🤖 AI-Powered Enhanced Tools

• Content Suggestions - AI-generated content ideas optimized for each platform
• Content Calendar - Strategic content planning with AI recommendations
• Audience Insights - Detailed demographics and engagement analysis
• Competitor Analysis - Track and analyze competitor strategies
• Advanced Hashtags - Smart hashtag optimization with difficulty scoring

🔧 Technical Features

• Multi-platform Support - Twitter, Instagram, Facebook, LinkedIn
• Real-time API Integration - Connect with actual social media APIs
• Data Persistence - Intelligent caching and data management
• Secure Authentication - Token-based security for API access
• Scalable Architecture - Built with FastMCP for high performance

📋 Prerequisites

• Python 3.8 or higher
• pip (Python package installer)
• ngrok (for external access)
• API keys for social media platforms (optional)

🛠️ Installation

1. Clone the Repository

git clone https://github.com/monu808/Social-Media-Management-Suite.git
cd Social-Media-Management-Suite/social-media-mcp-server

2. Install Dependencies

pip install -r requirements.txt

3. Configure Environment Variables

Create a .env file in the project root:

# MCP Authentication
AUTH_TOKEN=your_auth_token_here
MY_NUMBER=your_phone_number_here

# Twitter/X API (Optional)
TWITTER_BEARER_TOKEN=your_twitter_bearer_token
TWITTER_API_KEY=your_twitter_api_key
TWITTER_API_SECRET=your_twitter_api_secret
TWITTER_ACCESS_TOKEN=your_twitter_access_token
TWITTER_ACCESS_TOKEN_SECRET=your_twitter_access_token_secret

# Facebook/Instagram API (Optional)
FACEBOOK_APP_ID=your_facebook_app_id
FACEBOOK_APP_SECRET=your_facebook_app_secret
FACEBOOK_ACCESS_TOKEN=your_facebook_access_token

# LinkedIn API (Optional)
LINKEDIN_CLIENT_ID=your_linkedin_client_id
LINKEDIN_CLIENT_SECRET=your_linkedin_client_secret

# AI Services (Optional)
OPENAI_API_KEY=your_openai_api_key
RITEKIT_API_KEY=your_ritekit_api_key

# Analytics Services (Optional)
HOOTSUITE_API_KEY=your_hootsuite_api_key

4. Start the Server

python mcp_social_server.py

The server will start on http://0.0.0.0:8086

5. Set Up External Access (Optional)

For external access via Puch AI or other MCP clients:

ngrok http 8086

🎯 Usage

Basic Commands

Schedule a Post

await schedule_post(
    content="Your post content here",
    platforms="twitter,instagram",
    schedule_time="2024-12-25 14:30",
    media_url="https://example.com/image.jpg"  # optional
)

Generate Hashtags

await generate_hashtags(
    content="Your post content",
    platform="instagram",
    count=10
)

Get Analytics

await get_analytics(
    platform="all",
    timeframe="30d",
    metric_type="engagement"
)

Create Content Suggestions

await create_content_suggestion(
    platform="instagram",
    content_type="engagement",
    topic="technology"
)

Available MCP Tools

Tool	Description	Parameters
schedule_post	Schedule posts across platforms	content, platforms, schedule_time, media_url
generate_hashtags	Generate relevant hashtags	content, platform, count
get_analytics	Get engagement metrics	platform, timeframe, metric_type
get_trending_topics	Track trending topics	platform, category, location
manage_scheduled_posts	Manage scheduled posts	action, post_id
create_content_suggestion	AI content suggestions	platform, content_type, topic
create_content_calendar	Strategic content planning	platform, days, focus_topics
get_audience_insights	Audience analytics	platform, insight_type
manage_competitors	Competitor analysis	action, competitor_name, platforms
generate_advanced_hashtags	Advanced hashtag optimization	content, platform, count, strategy

🔌 Integration

With Puch AI (WhatsApp)

1. Start your server and ngrok
2. Get your ngrok URL: https://your-ngrok-url.ngrok-free.app
3. In WhatsApp, send:

   /mcp use https://your-ngrok-url.ngrok-free.app/mcp/ your_auth_token
   

With Other MCP Clients

Connect to http://your-server:8086/mcp/ with your authentication token.

📁 Project Structure

social-media-mcp-server/
├── mcp_social_server.py     # Main MCP server
├── requirements.txt         # Python dependencies
├── .env                    # Environment variables
├── README.md              # This file
├── .gitignore            # Git ignore rules
├── data/                 # Data storage
│   ├── scheduled_posts.json
│   ├── analytics_cache.json
│   └── trends_cache.json
└── utils/                # Utility modules
    ├── __init__.py
    ├── data_manager.py
    ├── social_apis.py
    ├── hashtag_engine.py
    ├── content_creator.py
    ├── audience_insights.py
    └── competitor_analysis.py

🔑 API Keys Setup

Twitter/X API

1. Create a Twitter Developer account
2. Create a new app and get your credentials
3. Add to .env file

OpenAI API

1. Sign up at OpenAI
2. Generate an API key
3. Add OPENAI_API_KEY to .env

Other APIs

• Facebook/Instagram: Meta Developer Platform
• LinkedIn: LinkedIn Developer Network
• RiteKit: Hashtag optimization service

🚨 Troubleshooting

Common Issues

Server Not Starting

• Check Python version (3.8+)
• Install dependencies: pip install -r requirements.txt
• Verify port 8086 is available

MCP Connection Failed

• Ensure server is running on correct port
• Check ngrok tunnel is active
• Verify authentication token matches

Missing Utility Modules

• All utility modules are included
• Check for import errors in logs
• Server works with mock data if modules fail

Debug Mode

Add debugging to your .env:

DEBUG=true
LOG_LEVEL=debug

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

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

🔗 Links

• FastMCP Documentation: https://gofastmcp.com
• MCP Specification: https://spec.modelcontextprotocol.io
• Puch AI: Connect via WhatsApp for easy access

📞 Support

For issues and questions:

• Create an issue on GitHub
• Check the troubleshooting section
• Review server logs for error details

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Built with ❤️ using FastMCP and Python

Empowering social media management through AI and automation