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How to build a product display scene from a short brief with Blender AI MCP Addon

Turn a written layout brief into a pedestal, camera, lights and matte material in Blender.

  • Content
  • Intermediate
  • 45 minutes
  • From the README
  • Any agent

Authorization notice. Blender AI MCP Addon is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA product designer or small studio artist who needs a quick display mockup for a client review.

The problemSetting up a basic product stage by hand means many repeated steps for primitives, lights, camera and materials.

The outcomeA simple product-display scene with a pedestal, camera, three lights and a matte blue material, checked in the viewport preview.

What you need first

  • Blender 4.2 or later; the release bundle targets Windows x64
  • The mcp-for-blender-pro.zip file from the GitHub Releases page
  • An account and API key for a supported provider (OpenAI, Anthropic Claude, Google Gemini, OpenRouter, OpenCode Zen), or a local Ollama install, or a compatible custom endpoint
  • Service keys for Poly Haven, Sketchfab or Poly Pizza if you want asset search and import

Steps

  1. Install and enable mcp-for-blender-pro in Blender, then open 3D Viewport, N, MCP Pro.
  2. Select a provider and model that support tool calling.
  3. Open Studio Window to see the chat, scene objects and viewport snapshots side by side.
  4. Send this message: Create a simple product-display scene with a pedestal, a camera and three lights. Give the pedestal a matte blue material.
  5. Check the viewport preview, then ask for corrections in the same conversation.

Prompt for your agent

Using the mcp-for-blender-pro add-on in Blender, build a product-display scene with a pedestal, one camera and three lights using the add_cube, add_cylinder, add_camera, add_light and set_material tools. Return the list of created object names. Verify with viewport_screenshot that the pedestal has the matte blue material.

Caveat. Results depend on the model you pick, so use a model with tool-calling support and expect to refine the output.

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