feat: Phase 2 - LLM Integration for Canvas

- Add canvas.ts MCP tool with validate_canvas_intent, execute_canvas_intent, interpret_canvas_intent
- Add useCanvasChat.ts bridge hook connecting canvas to chat system
- Update context_builder.py with canvas tool instructions
- Add ExecuteDialog for study name input
- Add ChatPanel for canvas-integrated Claude responses
- Connect AtomizerCanvas to Claude via useCanvasChat

Canvas workflow now:
1. Build graph visually
2. Click Validate/Analyze/Execute
3. Claude processes intent via MCP tools
4. Response shown in integrated chat panel

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

atomizer-dashboard/frontend/src/components/canvas/index.ts

@@ -2,4 +2,6 @@ export { AtomizerCanvas } from './AtomizerCanvas';
 export { NodePalette } from './palette/NodePalette';
 export { NodeConfigPanel } from './panels/NodeConfigPanel';
 export { ValidationPanel } from './panels/ValidationPanel';
+export { ExecuteDialog } from './panels/ExecuteDialog';
+export { ChatPanel } from './panels/ChatPanel';
 export * from './nodes';