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Clean the codebase
Remove unused files, dead code, debug logs, and ensure consistent formatting and structure.
Create a separate repository
Split the project into a dedicated repo for better maintainability, versioning, and collaboration.
Core Features & Improvements
Add PWA support
Enable Progressive Web App features (installability, offline support, caching, service workers).
Add JaSketch MCP server
Integrate JaSketch MCP server to enable drawing/canvas-based tool interactions.
Fix SSE transport method issue
Resolve instability or bugs in Server-Sent Events (SSE) communication to ensure reliable streaming between client and server.
AI - MCP Integration
Agent mode integration
Test MCP servers using real AI model calls to simulate how an actual LLM interacts with available tools.
Ensure tools behave correctly under real-world agent workflows (multi-step reasoning, tool chaining, etc.).
Platform Features
CORS handling (Admin Dashboard Feature – pending Musab)
Implement proper CORS configuration to allow users to connect and test locally hosted MCP servers via ProtoMCP.
This will enable seamless browser-based interaction without cross-origin issues.
Final Validation
End-to-end testing
Verify full flow: UI → MCP → AI → Tool execution → Response.
Documentation update
Ensure setup, usage, and architecture are clearly documented.
Deployment readiness
Confirm environment configs, build process, and hosting setup are production-ready.
Codebase Preparation
Clean the codebase
Remove unused files, dead code, debug logs, and ensure consistent formatting and structure.
Create a separate repository
Split the project into a dedicated repo for better maintainability, versioning, and collaboration.
Core Features & Improvements
Add PWA support
Enable Progressive Web App features (installability, offline support, caching, service workers).
Add JaSketch MCP server
Integrate JaSketch MCP server to enable drawing/canvas-based tool interactions.
Fix SSE transport method issue
Resolve instability or bugs in Server-Sent Events (SSE) communication to ensure reliable streaming between client and server.
AI - MCP Integration
Test MCP servers using real AI model calls to simulate how an actual LLM interacts with available tools.
Ensure tools behave correctly under real-world agent workflows (multi-step reasoning, tool chaining, etc.).
Platform Features
Implement proper CORS configuration to allow users to connect and test locally hosted MCP servers via ProtoMCP.
This will enable seamless browser-based interaction without cross-origin issues.
Final Validation
End-to-end testing
Verify full flow: UI → MCP → AI → Tool execution → Response.
Documentation update
Ensure setup, usage, and architecture are clearly documented.
Deployment readiness
Confirm environment configs, build process, and hosting setup are production-ready.