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>

mcp-server/atomizer-tools/src/index.ts

@@ -21,6 +21,7 @@ import { optimizationTools } from "./tools/optimization.js";
 import { analysisTools } from "./tools/analysis.js";
 import { reportingTools } from "./tools/reporting.js";
 import { physicsTools } from "./tools/physics.js";
+import { canvasTools } from "./tools/canvas.js";
 import { adminTools } from "./tools/admin.js";
 import { ATOMIZER_MODE } from "./utils/paths.js";
 
@@ -50,6 +51,7 @@ const userTools: AtomizerTool[] = [
   ...analysisTools,
   ...reportingTools,
   ...physicsTools,
+  ...canvasTools,
 ];
 
 const powerTools: AtomizerTool[] = [