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In order to give AI agents more flexible and powerful control over the tldraw canvas, this PR replaces the old server-side shape CRUD tools (create_shapes, update_shapes, delete_shapes) with an exec-based architecture where AI writes JavaScript that runs directly in the browser widget against a focused editor proxy. Reimplemented from [max/mcp-code-mode](https://github.com/tldraw/tldraw/tree/max/mcp-code-mode) with a clean commit history. Relates to #6832 **Key architectural changes:** - **Exec-based model**: AI writes JavaScript code that executes in the browser via a sandboxed `executeCode()` function, with access to the full Editor API through a focused editor proxy - **Focused editor proxy**: ES Proxy that transparently converts between AI-friendly focused shapes (simple string IDs, named colors, pixel font sizes) and tldraw's internal representation - **Search tool**: AI can query the Editor API spec (extracted at build time from TypeScript declarations) to discover methods, shape types, and helpers - **Server-widget callback bridge**: `PendingRequests` system enables async request-response between the Cloudflare Worker and browser widget - **Build-time API extraction**: TypeScript compiler API script reflects on tldraw packages to generate searchable API metadata and method classification maps ### Change type - [x] `improvement` ### Test plan 1. Deploy the MCP app and connect from Claude Desktop 2. Ask AI to create shapes — should use exec tool with focused editor proxy 3. Ask AI to search for Editor methods — should use search tool 4. Verify checkpoints persist across reconnections 5. Test in Cursor, VS Code, and ChatGPT hosts ### Release notes - Migrate MCP app from shape CRUD tools to exec-based architecture for more flexible AI canvas control ### Code changes | Section | LOC change | | -------------- | ---------------- | | Apps | +4504 / -2375 | | Documentation | +23 / -39 | | Config/tooling | +9 / -134 |
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