neroyoshi/Development_Hub

🎯 VesicaPiscus Development Hub

🛠️ MCP Server Ecosystem voor AI-gestuurde Artistieke Creatie

🌀 Project Essentie

Dit is de Development Hub voor het VesicaPiscus Synergistisch Atelier - een volledig geïntegreerd MCP server ecosysteem met 38 operationele tools voor AI-gestuurde kunst creatie en commercialisering.

🧬 Twee Primaire Motoren:

1. 🎭 Concept-to-Canvas (C2C) Motor: Cyclische, exploratieve workflow voor artistieke co-creatie

   • FCR: VAE Latente Ruimte (dynamische mogelijkheidsruimte)
   • SQS: Artistieke Synergie Index (ASI)

2. 💼 Art-to-Market (A2M) Motor: Lineaire, geautomatiseerde commercialisering

   • FCR: Artwork DNA (YAML metadata in Obsidian)
   • SQS: Brand Resonance & Engagement Score (BRES)

🤖 AI Agent Crews:

• Creative Crew: Maestro, Muze, Visionair, Archivaris (C2C focus)
• Commercial Crew: Curator, Kunstcriticus, Scribent, SEO Specialist, Webmaster, Publicist (A2M focus)

🎯 Evolutie naar Anima Gemella:

De ultieme vorm is een proactieve, data-gedreven muze die beide motoren fuseert voor creatieve paden die zowel artistiek resonant als commercieel veelbelovend zijn.

📁 Project Structuur

Development_Hub/
├── vesica_mcp_server/          # 🧠 Central MCP API Server
│   ├── main.py                 # Extended Metatron server + Vesica tools
│   ├── wordpress_client.py     # WordPress/WooCommerce integration
│   ├── artwork_dna_parser.py   # YAML frontmatter processing
│   └── postiz_client.py        # Social media automation
├── creative_crew_agents/       # 🤖 CrewAI Agent Definitions
│   ├── commercial_crew/        # Art-to-Market (A2M) agents
│   └── creative_crew/          # Concept-to-Canvas (C2C) agents
├── orchestrator_service/       # 🎯 Event-driven coordination
│   ├── event_handlers/         # Obsidian/filesystem watchers
│   └── workflow_engine/        # HOTL workflow management
├── vae_training/              # 🧬 VAE Latent Space Training
│   ├── data_preprocessing/     # Image preparation
│   ├── model_architecture/      # β-VAE with Perceptual Loss
│   └── training_pipelines/     # M2 Max optimized training
├── frontend_apps/             # 🖥️ Web interfaces
│   ├── artist_dashboard/       # Creative workflow UI
│   └── admin_panel/            # System monitoring
├── deployment/                # 🚀 Infrastructure & DevOps
│   ├── docker/                # Container definitions
│   └── configs/               # Environment configurations
└── docs/                      # 📚 Technical documentation
    ├── api_reference/         # MCP server endpoints
    └── architecture_diagrams/ # System design docs

🔗 Integration Points

• Content Hub: /Users/nero/CascadeProjects/Vesica_Workspace (Obsidian kennishub)
• Generic Tooling: /Users/nero/CascadeProjects/Metatron_Workspace (reusable components)

🚀 Current Development Status

✅ Phase 1: Foundation (COMPLETED)

• MCP Server: Operationeel op http://127.0.0.1:8001 met 38 MCP Tools
• Memory MCP: 9 tools (persistent knowledge graph)
• Filesystem MCP: 6 tools (secure file operations)
• Git MCP: 8 tools (version control automation) 🆕
• GitHub MCP: 6 tools (development workflow)
• Obsidian MCP: 2 tools (knowledge vault integration)
• OpenProject MCP: 5 tools (project management)
• Sequential Thinking MCP: 1 tool (complex reasoning)
• WordPress Integration: Full REST API client (posts, media, WooCommerce)
• Artwork DNA Parser: YAML frontmatter extraction & validation
• Environment Setup: .env configurations & dependency management

🎯 Phase 2: Agent Framework (CURRENT FOCUS)

• CrewAI Integration: Commercial Crew implementation
• Basic Orchestration: File-based trigger system
• Agent Tools: MCP server tool integration
• Testing Pipeline: End-to-end A2M workflow validation

🔮 Phase 3: ML Pipeline (FUTURE)

• VAE Training: Custom β-VAE with Perceptual Loss
• CogVLM Finetuning: Vision Language Model for art analysis
• CoreML Conversion: Optimized inference on Apple Silicon
• C2C Workflow: Complete Creative Crew implementation

📋 Immediate Next Steps:

1. Setup Commercial Crew agents (Kunstcriticus, Scribent, Webmaster)
2. Implement basic Orchestrator service with file watchers
3. Test complete A2M pipeline with real Artwork DNA
4. Expand MCP server with ontology-specific tools

• Development: This workspace (technical implementation)

🚀 Quick Start

1. Setup MCP Server: cd vesica_mcp_server && pip install -r requirements.txt
2. Configure Environment: Copy .env.example to .env and configure
3. Run Development Server: python3 main.py (serves on http://127.0.0.1:8001)
4. Test Integration: Access Swagger UI at http://127.0.0.1:8001/docs
5. Validate Tools: curl http://127.0.0.1:8001/mcp/tools | jq '.tools | length' (should return 38)

🎯 Current Development Status

• Base project structure created
• Metatron MCP server copied and ready for extension
• Memory MCP integration (9 tools - persistent knowledge graph)
• Filesystem MCP integration (6 tools - secure file operations)
• Git MCP integration (8 tools - version control automation) 🆕
• GitHub MCP integration (6 tools - development workflow)
• Obsidian MCP integration (2 tools - knowledge vault)
• OpenProject MCP integration (5 tools - project management)
• Sequential Thinking MCP (1 tool - complex reasoning)
• WordPress/WooCommerce integration
• Artwork DNA parser implementation
• CrewAI agent definitions
• HOTL workflow management
• VAE training pipeline setup

📞 Next Steps

MCP Server Foundation Complete - Ready for CrewAI Agent Development:

1. Implement Commercial Crew agents with Memory MCP integration
2. Setup Orchestrator service with file watchers and event triggers
3. Test complete A2M pipeline with automated Git workflows
4. Integrate CrewAI agents with all 38 MCP tools
5. Implement HOTL approval gates with persistent memory

Key Git MCP Capabilities for Agents:

• Automated version control for generated content
• Branch management for collaborative agent workflows
• Commit history analysis and documentation
• Secure repository access with path validation

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Built with the Metatron Constitutie: FCR (Source of Truth), SQS (Quality Maximization), Universum-Adem (Operational Cadence)