Integrations

Uses environment files to configure credentials for Google Ads API and other service connections
Provides repository hosting for the app-seo-ai codebase, with integration instructions for cloning and deployment
Integrates with Google Ads API for keyword research, allowing users to discover related keywords for SEO optimization through the research_keywords tool

App SEO AI

Application for SEO automation and AI-powered optimization with Google Ads Keyword Planner integration.

Features

Keyword research using Google Ads API
SERP analysis
Competitor analysis
SEO recommendations
MCP (Model Context Protocol) integration for AI assistants

Prerequisites

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

Setup

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. Get Google Ads API refresh token

Run the following command to get 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 start on the port specified in your .env file (default: 3000).

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 in the mcp.json file.

To use this with Smithery:

Go to Smithery
Create a new MCP server
Select the app-seo-ai repository
Configure the server settings
Deploy the server

Available MCP Tools

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

Example 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

MIT

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MCP server that enables AI assistants to perform SEO automation tasks including keyword research, SERP analysis, and competitor analysis through Google Ads API integration.

Appeared in Searches

Search Engine Optimization (SEO) Resources