App Seo Ai

🚀 SEO AI Application

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

🚀 Quick Start

The SEO AI Application is designed for automating SEO tasks and leveraging AI for optimization. It integrates with the Google Ads API to provide comprehensive SEO solutions.

✨ Features

• Conduct keyword research using the Google Ads API.
• Analyze Search Engine Results Pages (SERPs).
• Perform competitor analysis.
• Offer SEO 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

Installation 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 your .env file.

5. Start the server

For development:

npm run dev

For production:

npm start

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

💻 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));

📚 Documentation

API Documentation

The API documentation is available at the /api-docs path 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, follow these steps:

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 specific query.
• analyze_competitors - Analyze competitors for a given keyword or domain.
• _health - Health check endpoint.

📄 License

This project is licensed under the MIT License.