SEO AI (Google Ads Keyword Planner)

🚀 App SEO AI

An application for SEO automation and AI optimization, integrating the Google Ads Keyword Planner tool.

This application streamlines SEO processes by leveraging Google Ads API and AI capabilities, offering comprehensive solutions for keyword research, SERP analysis, and competitor insights.

🚀 Quick Start

App SEO AI is a powerful tool that combines SEO automation with AI optimization. It integrates with Google Ads API to provide in - depth keyword research and other SEO features.

✨ Features

• Conduct keyword research using the Google Ads API.
• Perform Search Engine Results Page (SERP) analysis.
• Analyze competitors.
• Provide SEO optimization suggestions.
• Integrate with MCP (Model Context Protocol) for AI assistants.

📦 Installation

Prerequisites

• Node.js (v14 or higher)
• npm or yarn
• A Google Ads account with API access
• A Google Cloud Platform project with the Google Ads API enabled

Steps

1. Clone the repository

git clone https://github.com/ccnn2509/app-seo-ai.git
cd app-seo-ai

2. Install dependencies

npm install

3. Configure environment variables

Copy the example environment file:

cp .env.example .env

Edit the .env file and fill in your Google Ads API credentials:

# Server configuration
PORT=3000
NODE_ENV=development

# Google Ads API configuration
GOOGLE_ADS_DEVELOPER_TOKEN=your_developer_token
GOOGLE_ADS_CLIENT_ID=your_client_id
GOOGLE_ADS_CLIENT_SECRET=your_client_secret
GOOGLE_ADS_REFRESH_TOKEN=your_refresh_token
GOOGLE_ADS_LOGIN_CUSTOMER_ID=your_customer_id_without_dashes

# SERP API configuration (optional)
SERP_API_KEY=your_serp_api_key

4. Obtain a Google Ads API refresh token

Run the following command to obtain a refresh token:

npm run get-token

This will open your browser and guide you through the OAuth2 authentication process. The refresh token will be automatically saved to the .env file.

5. Start the server

In development environment:

npm run dev

In production environment:

npm start

The server will run on the port specified in your .env file (default: 3000).

📚 Documentation

API Documentation

API documentation is available at /api-docs when the server is running:

http://localhost:3000/api-docs

MCP Integration

This project includes MCP (Model Context Protocol) integration, allowing AI assistants to use the API. The MCP configuration is located in the mcp.json file.

To use it with Smithery:

1. Visit Smithery
2. Create a new MCP server
3. Select the app-seo-ai repository
4. Configure the server settings
5. Deploy the server

Available MCP Tools

• research_keywords - Research keywords related to a given topic or seed keyword.
• analyze_serp - Analyze the Search Engine Results Page (SERP) for a specified query.
• analyze_competitors - Analyze competitors for a specified keyword or domain.
• _health - Health check endpoint.

💻 Usage Examples

Basic Usage

Research Keywords

// Example request to research keywords
fetch('http://localhost:3000/api/keywords/ideas?keyword=seo%20tools&language=en')
  .then(response => response.json())
  .then(data => console.log(data));

Analyze SERP

// Example request to analyze SERP
fetch('http://localhost:3000/api/serp/analyze?query=best%20seo%20tools&location=United%20States')
  .then(response => response.json())
  .then(data => console.log(data));

Analyze Competitors

// Example request to analyze competitors
fetch('http://localhost:3000/api/competitors/analyze?domain=example.com')
  .then(response => response.json())
  .then(data => console.log(data));

📄 License

This project is licensed under the MIT license.