Evaluate: using inscope as a central place to consolidate all of our various AI projects across GE.  General idea is to use the Catalog, Docs, and Learning Paths aspects to help associates add their AI projects, MCP servers, and learning info in a canonical place.

Requirements

Platform Overview

An internal AI project sharing hub built on Red Hat Developer Hub (RHDH) that serves as a catalog for AI-related projects developed by Global Engineering teams. The platform enables discovery, sharing, and collaboration on internal AI tools, MCP servers, scripts, and software projects.

Core Functional Requirements

1. Project Catalog Management

• Project Registry: Maintain a catalog of AI-related projects from Global Engineering teams
• Project Discovery: Enable users to browse and search through the entire project catalog
• Advanced Filtering: Support filtering by:
  • Project category (MCP Servers, Scripts, Tools, Applications, etc.)
  • Use case (Data Processing, Automation, Analytics, etc.)
  • Technology stack (Python, Node.js, Go, etc.)
  • Team/Organization
  • Project status (Active, Deprecated, Experimental)
• Search Capabilities: Full-text search across project names, descriptions, and metadata

2. Self-Service Project Submission

• Minimal Friction Onboarding: Provide a simple, intuitive interface for teams to add projects
• Repository Integration: Leverage existing Git repositories without requiring migration or duplication
• External Project Submission: Allow users to add projects they do not have direct commit access to (e.g., external repositories like https://github.com/sooperset/mcp-atlassian) without requiring any modifications to the source project
• Metadata Extraction: Automatically extract relevant project information from (examples):
  • README files
  • Package.json, requirements.txt, go.mod, etc.
  • Git commit history and contributors
  • Repository structure and file types
• Manual Metadata Enhancement: Allow optional additional metadata entry for (examples):
  • Project description and use cases
  • Category classification
  • Contact information
  • Documentation links
  • Demo/screenshot URLs

3. User Experience & Interface

• Intuitive Navigation: Clear, logical site structure and navigation
• Project Rating System: Allow users to like/dislike and/or rate projects to provide feedback and help others discover quality projects
• Natural Language Interface: Provide a conversational AI interface that allows users to chat with documentation, project information, and platform features to help discover and learn about projects through natural language queries
• Project Comparison: Side-by-side comparison of similar projects

Technical Requirements

5. Red Hat Developer Hub Integration

• RHDH Plugin Architecture: Leverage RHDH's plugin system for custom functionality
• Backstage Integration: Utilize Backstage's software catalog capabilities
• Authentication & Authorization: Integrate with existing corporate identity systems

6. Data Management

• Repository Scanning: Automated discovery and indexing of AI-related repositories
• Metadata Synchronization: Regular updates to keep project information current
• Data Validation: Ensure project metadata accuracy and completeness
• Backup & Recovery: Reliable data backup and disaster recovery procedures

7. Integration Requirements

• Git Platform Integration: Support for multiple Git hosting platforms used by Global Engineering
• Documentation Integration: Link to existing documentation platforms (Confluence, etc.)

Future Enhancements (Phase 2+)

• Project Cards: Display key project information in an easily scannable format
• Detailed Project Pages: Comprehensive project views including:
  • Project description and purpose
  • Installation/usage instructions
  • Technology stack and dependencies
  • Contributing guidelines
  • License information
  • Recent activity and updates
• Live Repository Data: Real-time integration with source repositories for:
  • Latest commit information
  • Issue/PR status
  • Release information
  • Contributor activity
• Project Templates: Standardized templates for common AI project types
• AI-Powered Recommendations: Machine learning-based project suggestions
• Recommendation Engine: Suggest related projects based on technology stack or use case
• Integration Marketplace: Easy deployment/integration options for discovered projects
• Analytics Dashboard: Usage analytics and project popularity metrics