Google Ads MCP Server

🚀 Google Ads MCP Server

A comprehensive platform for managing and optimizing Google Ads accounts, supporting various functional modules and services.

🚀 Quick Start

The Google Ads MCP Server is a comprehensive platform designed to streamline the management and optimization of Google Ads accounts. It offers a wide range of features and tools to enhance advertising performance and efficiency.

✨ Features

Campaign Management

• Automated Optimization: Utilize machine learning algorithms to optimize ad placement strategies, enhancing conversion rates and click - through rates.
• Budget Control: Intelligently allocate daily budgets to ensure the best return on investment (ROI).
• Multi - Account Management: Support simultaneous management of multiple Google Ads accounts through a unified management interface.

Keyword Tools

• Automated Keyword Mining: Automatically generate high - value keyword suggestions by analyzing search data.
• Competition Analysis: Evaluate the competitiveness of target keywords and provide optimization suggestions.
• Keyword Grouping: Intelligently recommend keyword grouping strategies to improve ad placement efficiency.

Conversion Tracking

• Multi - Channel Attribution Model: Accurately identify the contribution of different advertising channels.
• Conversion Path Analysis: Visualize user conversion paths to assist in optimizing advertising strategies.
• Real - Time Monitoring: Continuously track the effectiveness of advertising campaigns and adjust placement strategies promptly.

🔧 Technical Details

Core Components

Campaign Manager

Responsible for creating, modifying, and deleting advertising campaigns, supporting batch processing functions.

Budget Controller

Dynamically allocate daily budgets to optimize the efficiency of fund usage across different advertising campaigns.

Data Analysis Engine

Integrate multiple data sources to provide comprehensive advertising effectiveness analysis reports, helping users make informed decisions.

System Architecture

graph LR
    A[User Request] -->|HTTP| B(Front - end Server)
    B -->|API Call| C(Google Ads API)
    C -->|Data Processing| D[Data Analysis Engine]
    D -->|Result Storage| E[Database]
    E -->|Feedback to User| B

📦 Installation

Environment Configuration

• Development Environment: kubernetes/dev/
• Testing Environment: kubernetes/test/
• Production Environment: kubernetes/prod/

CI/CD Process

Use GitHub Actions to implement automated testing, building, and deployment to ensure code quality.

🛡️ Security Measures

• All credentials are stored in Kubernetes secrets or environment variables, never in plain text.
• Implement a strict API access authentication mechanism on the server - side.
• Enable rate - limiting in the production environment.
• Follow container security best practices.

💻 Usage Examples

Basic Usage

Run the Test Suite

pytest

Execute Specific Tests

# Ad group function test
python -m google_ads_mcp_server.tests.unit.test_ad_groups

# Keyword function test
python -m google_ads_mcp_server.tests.unit.test_keywords

# Budget function test
python -m google_ads_mcp_server.tests.unit.test_budgets

# MCP tool test
python -m google_ads_mcp_server.tests.unit.test_tools

# Visualization formatter test
python -m google_ads_mcp_server.tests.unit.test_visualizations

# Budget visualization test
python -m google_ads_mcp_server.tests.unit.test_budget_visualizations

🤝 Contribution Guide

1. Fork the repository:

git fork

2. Create a feature branch:

git checkout -b feature/my - feature

3. Commit changes:

git commit -am 'Add new feature'

4. Push to the remote repository:

git push origin feature/my - feature

5. Submit a pull request.

📄 License

This project is licensed under the MIT License. Please refer to the LICENSE file for details.

🙏 Acknowledgments

Thanks to the Google Ads API team for their documentation and support, and thanks to Anthropic, its Claude model, and the Model Context Protocol.