AIOptimizr-SEO-Engine

System for autonomous SEO operations and data-driven content enhancement, featuring native connectivity to the Google Ads Keyword Planner service.

Core Capabilities

• Keyword Intelligence Acquisition: Utilization of the Google Ads API for deep keyword investigation.
• Search Engine Results Page (SERP) Diagnostics: In-depth analysis of current search rankings.
• Competitive Landscape Mapping: Systematic evaluation of rival domain strategies.
• Strategic Optimization Directives: Generation of prioritized, actionable SEO improvement tasks.
• Model Context Protocol (MCP) Conduit: Built-in framework facilitating seamless interaction with AI co-pilots.

Prerequisites for Deployment

• Runtime Environment: Node.js (minimum version 14.x)
• Package Manager: npm or yarn
• Credentials: Valid Google Ads account necessitating API access authorization.
• Cloud Setup: Configured Google Cloud Platform project with the Google Ads API enabled and accessible.

Implementation Guide

Step 1: Repository Acquisition

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

Step 2: Dependency Resolution

npm install

Step 3: Environment Parameter Configuration

Duplicate the template configuration file:

cp .env.example .env

Modify the .env file, populating it with your proprietary Google Ads API authentication material:

# Server Operational Parameters
PORT=3000
NODE_ENV=development

# Google Ads Credential Block
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 Data Ingestion Key (Optional)
SERP_API_KEY=your_serp_api_key

Step 4: Obtaining the OAuth Refresh Token

Execute the following utility command to finalize token acquisition:

npm run get-token

This action will securely initiate a browser session for OAuth2 authorization; the resultant refresh token will be appended automatically to your .env file.

Step 5: Service Initialization

For iterative development cycles:

npm run dev

For production deployment:

npm start

The core application service will commence operation on the port defined in the environment settings (default is 3000).

Interface Documentation Reference

Comprehensive documentation for the platform's exposed endpoints is dynamically generated and accessible at /api-docs once the service is active:

http://localhost:3000/api-docs

MCP Interoperability Layer

This deployment incorporates the Model Context Protocol (MCP) specification, enabling advanced AI agents to interface programmatically with the backend services. The specific MCP configuration schema resides within the mcp.json manifest.

To integrate this system with the Smithery orchestration framework:

1. Navigate to the Smithery Platform
2. Initiate the creation of a novel MCP service endpoint.
3. Designate the app-seo-ai repository as the source artifact.
4. Fine-tune the service operational parameters.
5. Execute the final deployment sequence.

Exposed MCP Functions

• research_keywords - Executes sophisticated keyword opportunity identification based on specified thematic inputs or initial seed terms.
• analyze_serp - Performs detailed diagnostic examination of the Search Engine Results Page for a given search string.
• analyze_competitors - Systematically profiles the digital performance metrics of rival entities based on keyword sets or domain names.
• _health - A basic operational status verification endpoint.

Operational Examples (Client-Side Fetch)

Keyword Investigation Module

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

SERP Deconstruction Module

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

Competitor Profiling Module

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

Licensing Framework

MIT