# Marketing Automation MCP Documentation
Welcome to the comprehensive documentation for the Marketing Automation MCP (Model Context Protocol) server. This system provides AI-powered tools for automating marketing workflows, optimizing campaigns, and maximizing ROI.
## 🚀 Quick Links
- **[Quick Start Guide](./quickstart.md)** - Get up and running in 30 minutes
- **[API Reference](./api/README.md)** - Detailed API documentation
- **[Example Workflows](./examples/README.md)** - Practical implementation examples
- **[ROI Methodology](./guides/roi-methodology.md)** - How we calculate and track ROI
## 📋 Table of Contents
1. [Overview](#overview)
2. [Architecture](#architecture)
3. [Features](#features)
4. [Installation](#installation)
5. [Configuration](#configuration)
6. [Usage](#usage)
7. [Integration](#integration)
8. [Best Practices](#best-practices)
9. [FAQ](#faq)
## Overview
The Marketing Automation MCP server is a comprehensive solution for automating marketing tasks using AI. It integrates with major advertising platforms (Google Ads, Facebook Ads) and analytics tools (Google Analytics) to provide intelligent optimization and automation capabilities.
### Key Benefits
- **Time Savings**: Automate tasks that typically take hours in just seconds
- **Cost Reduction**: Save 80-95% on labor costs for routine marketing tasks
- **Performance Improvement**: AI-driven optimization typically improves ROI by 20-40%
- **Scalability**: Manage hundreds of campaigns with the same effort as managing one
- **Consistency**: Ensure consistent optimization decisions based on data
## Architecture
```
┌─────────────────────────────────────────────────────────┐
│ MCP Client │
│ (Your Application/Claude) │
└─────────────────────┬───────────────────────────────────┘
│ MCP Protocol
┌─────────────────────┴───────────────────────────────────┐
│ Marketing Automation MCP Server │
├─────────────────────────────────────────────────────────┤
│ MCP Tools Layer │
│ ┌─────────────┐ ┌──────────────┐ ┌─────────────────┐ │
│ │ Report │ │ Budget │ │ Copy │ │
│ │ Generation │ │ Optimization │ │ Generation │ │
│ └─────────────┘ └──────────────┘ └─────────────────┘ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Audience Segmentation │ │
│ └─────────────────────────────────────────────────┘ │
├─────────────────────────────────────────────────────────┤
│ Core Services Layer │
│ ┌────────────┐ ┌──────────────┐ ┌─────────────────┐ │
│ │ AI Engine │ │ Integrations │ │ Database │ │
│ │ (OpenAI) │ │ (APIs) │ │ (SQLAlchemy) │ │
│ └────────────┘ └──────────────┘ └─────────────────┘ │
│ ┌────────────┐ ┌──────────────┐ │
│ │ Reporting │ │ Tracking │ │
│ │ (Plotly) │ │ (ROI) │ │
│ └────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────┘
│ │ │
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Google │ │ Facebook │ │ Google │
│ Ads │ │ Ads │ │Analytics │
└──────────┘ └──────────┘ └──────────┘
```
## Features
### 🛠️ MCP Tools
1. **generate_campaign_report**
- Comprehensive performance reports
- Multiple format support (JSON, HTML, PDF, CSV)
- Customizable metrics and visualizations
- AI-powered insights
2. **optimize_campaign_budget**
- AI-driven budget allocation
- Multiple optimization goals
- Constraint handling
- Performance projections
3. **create_campaign_copy**
- AI-generated ad copy
- Platform-specific optimization
- A/B testing variants
- Tone and style customization
4. **analyze_audience_segments**
- Intelligent segmentation
- Value scoring
- Overlap analysis
- Campaign recommendations
### 🔌 Integrations
- **Google Ads**: Full campaign management capabilities
- **Facebook Ads**: Campaign and audience management
- **Google Analytics**: Performance tracking and analysis
- **Unified Client**: Manage all platforms from one interface
### 🤖 AI Capabilities
- **Performance Analysis**: Identify trends and opportunities
- **Optimization Suggestions**: Data-driven recommendations
- **Content Generation**: Create compelling marketing copy
- **Predictive Analytics**: Forecast campaign performance
### 📊 Reporting & Analytics
- **Automated Reports**: Weekly summaries, ROI analysis
- **Custom Dashboards**: Executive-level insights
- **Performance Tracking**: Real-time monitoring
- **ROI Calculation**: Comprehensive cost-benefit analysis
## Installation
### Prerequisites
- Python 3.8+
- API credentials for desired platforms
- OpenAI API key
### Basic Installation
```bash
# Clone repository
git clone https://github.com/your-org/marketing-automation-mcp.git
cd marketing-automation-mcp
# Create virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Edit .env with your credentials
# Run the server
python -m src.server
```
For detailed installation instructions, see the [Quick Start Guide](./quickstart.md).
## Configuration
### Environment Variables
```bash
# Required
OPENAI_API_KEY=your_openai_api_key
# Platform Credentials (at least one required)
GOOGLE_ADS_DEVELOPER_TOKEN=...
FACEBOOK_APP_ID=...
GOOGLE_ANALYTICS_PROPERTY_ID=...
# Optional
DATABASE_URL=sqlite:///marketing_automation.db
LOG_LEVEL=INFO
```
### Platform Setup
Each platform requires specific setup:
1. **Google Ads**: OAuth2 setup with developer token
2. **Facebook Ads**: App creation and access token
3. **Google Analytics**: Property ID and OAuth2
4. **OpenAI**: API key from OpenAI dashboard
## Usage
### Basic Example
```python
from mcp import Client
async with Client("marketing-automation") as client:
# Generate report
report = await client.call_tool(
"generate_campaign_report",
{
"campaign_ids": ["camp_001"],
"date_range": {"start": "2024-01-01", "end": "2024-01-31"},
"metrics": ["clicks", "conversions", "roi"]
}
)
```
### Advanced Workflows
See [Example Workflows](./examples/README.md) for:
- Multi-platform campaign management
- AI-driven budget reallocation
- Automated reporting pipelines
- Complete automation cycles
## Integration
### MCP Protocol
The server implements the Model Context Protocol, making it compatible with:
- Claude Desktop
- Any MCP-compatible client
- Custom applications using the MCP SDK
### Direct API Usage
Components can be used directly:
```python
from src.ai_engine import MarketingAIEngine
from src.integrations.unified_client import UnifiedMarketingClient
# Use components in your application
engine = MarketingAIEngine()
client = UnifiedMarketingClient()
```
## Best Practices
### 1. Start Conservative
- Begin with small budget changes (5-10%)
- Test on a subset of campaigns
- Monitor results closely
### 2. Track Everything
- Use AutomationTracker for all tasks
- Monitor ROI metrics regularly
- Document automation decisions
### 3. Validate AI Output
- Review AI recommendations
- Start with high-confidence suggestions
- A/B test generated content
### 4. Scale Gradually
- Automate one workflow at a time
- Increase automation scope based on results
- Maintain human oversight
## FAQ
### Q: What platforms are supported?
A: Currently Google Ads, Facebook Ads, and Google Analytics. More platforms can be added through the integration framework.
### Q: How accurate are the AI predictions?
A: Prediction accuracy typically ranges from 75-90% depending on data quality and campaign history. The system provides confidence scores for all predictions.
### Q: Can I customize the AI prompts?
A: Yes, the AI engine uses a template system that can be customized. See the AI Engine documentation for details.
### Q: How is ROI calculated?
A: See our comprehensive [ROI Methodology](./guides/roi-methodology.md) document for detailed calculations.
### Q: Is my data secure?
A: All data is processed locally, API credentials are stored securely, and no campaign data is sent to external services except for the configured platforms and OpenAI (for AI features).
### Q: Can I add custom integrations?
A: Yes, the system is designed to be extensible. Implement the BaseIntegrationClient interface to add new platforms.
## Support & Contributing
- **Issues**: Report bugs on [GitHub Issues](https://github.com/your-org/marketing-automation-mcp/issues)
- **Discussions**: Join our [community forum](https://forum.example.com)
- **Contributing**: See [CONTRIBUTING.md](../CONTRIBUTING.md)
## License
This project is licensed under the MIT License. See [LICENSE](../LICENSE) for details.
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