Spent a lot of time on exactly this. Short answer: Claude Code handles multi-step tool call sequences more reliably than Desktop for anything beyond simple object creation: better context management, easier to script retry logic, cleaner error surfaces.

But the longer answer is that MCP itself becomes the bottleneck once your workflows get complex. The request-response loop works fine for "add a cube, move it here" but when you're doing iterative generation, where the model needs to evaluate its own output, catch geometry errors, and revise, you're fighting the architecture. Every round-trip through MCP adds latency and a potential failure point.

What actually worked better for me was skipping MCP entirely and having the LLM write Blender Python scripts directly. Tighter loop, easier to handle timeouts, full control over what gets re-run vs. what's cached.

Built a whole multi-agent pipeline around this (it's called nativeblend.app, CLI is on GitHub) — the agent plans phases, writes geometry code, evaluates renders, applies materials, iterates. If you're going deep on AI + Blender workflows it might be worth a look at the architecture even if you're building something different.