AI-Driven Digital Visibility Management Suite

Application dedicated to automating SEO processes and implementing AI-enhanced optimization routines, featuring deep integration with the Google Ads Keyword Planner infrastructure.

Core Functionalities

• Query Term Discovery leveraging the Google Ads Data Interface
• Search Engine Results Page (SERP) landscape evaluation
• Rival enterprise performance auditing
• Prescriptive Search Engine Optimization directives
• Native integration support for the Model Context Protocol (MCP) facilitating AI agent interaction

Necessary Prerequisites

• Runtime environment: Node.js (version 14 or greater is mandated)
• Package manager: npm or yarn package manager utility
• Authentication profile: Active Google Ads account granting API access permissions
• Cloud setup: Configured Google Cloud Platform project with the Google Ads API feature enabled

Deployment Procedure

Step 1: Source Code Retrieval

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

Step 2: Dependency Installation

bash npm install

Step 3: Environmental Variable Configuration

Duplicate the template configuration file:

bash cp .env.example .env

Modify the newly created .env file to input your proprietary Google Ads API access credentials:

Server Runtime Settings

PORT=3000 NODE_ENV=development

Google Ads API Credentials

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 Source Configuration (Optional)

SERP_API_KEY=your_serp_api_key

Step 4: Acquiring the OAuth2 Refresh Token

Execute the following command to initiate the process for obtaining a valid refresh token:

bash npm run get-token

This action launches a browser interface to facilitate the OAuth2 authorization handshake. The resulting refresh token is subsequently persisted within the .env configuration file automatically.

Step 5: Initiating the Service

For iterative development cycles:

bash npm run dev

For stabilized production deployment:

bash npm start

The service instance will commence operation on the network port defined in the configuration (default setting: 3000).

Interface Specification Documentation

Comprehensive API documentation is reachable via the /api-docs path once the service is operational:

http://localhost:3000/api-docs

MCP Interoperability Layer

This application includes built-in support for the Model Context Protocol (MCP), enabling external artificial intelligence assistants to programmatically interact with its exposed functionality. The specific MCP configuration metadata resides in the mcp.json manifest.

To deploy and utilize this service with Smithery:

1. Navigate to Smithery
2. Instantiate a novel MCP endpoint service
3. Designate the app-seo-ai repository as the source module
4. Calibrate the service operational parameters
5. Finalize deployment

Accessible MCP Operands

• research_keywords - Executes comprehensive keyword research based on a provided conceptual theme or initial seed term.
• analyze_serp - Performs detailed analysis of the Search Engine Results Page corresponding to a specified information retrieval query.
• analyze_competitors - Conducts an assessment of competitive entities relative to a designated keyword set or operational domain name.
• _health - Standard endpoint for verifying service availability and operational status.

Operational Demonstration Examples

Query Term Investigation

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

SERP Examination

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

Competitive Review

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

Licensing Terms

Distributed under the MIT License Agreement.