AIAny
Icon for item

BlenderMCP

Runs an MCP server that lets an LLM like Claude drive Blender directly: create and edit objects, apply materials, inspect scenes, and run Python. Pulls assets from Sketchfab, Poly Haven, and Hyper3D so prompts build editable 3D scenes.

Introduction

Text-to-3D demos usually generate a static mesh you can't touch afterward. This takes the opposite path: instead of producing a model, it hands the AI the keys to the editor itself. The model issues real Blender operations through a Model Context Protocol bridge, so every object, material, and modifier stays a live, editable part of your scene rather than a frozen export.

What Sets It Apart
  • Two-way socket, not one-shot generation — the LLM inspects the current scene, acts, then re-inspects, so it can iterate and correct mistakes instead of regenerating from scratch.
  • Arbitrary Python execution — anything the Blender Python API can do, the agent can do, so it isn't boxed into a fixed set of primitives.
  • Asset pipelines built in — Sketchfab, Poly Haven, Hyper3D, and Hunyuan3D let prompts pull or generate real models, so a scene can be furnished, not just blocked out.
  • Editor-native results — output lands as normal Blender data you keep tweaking by hand.

It helped kick off the broader "creative-tool MCP" wave and crossed 11k stars within weeks of release.

Who It's For

Great fit if you already live in Blender and want to offload tedious setup, blocking, or asset wrangling to a prompt while keeping full manual control. Look elsewhere if you want a turnkey text-to-3D generator with no software to learn — this assumes Blender fluency, and the agent's raw Python access means a careless prompt can still mangle your scene.

Information

  • Websitegithub.com
  • OrganizationsSiddharth Ahuja
  • AuthorsSiddharth (ahujasid)
  • Published date2025/03/07

More Items

GitHub

Connects Blender to any LLM so models can inspect, create, and modify 3D scenes via a socket-based Model Context Protocol (MCP) server; supports object manipulation, materials, renders, asset import, and executing Blender Python.

GitHub
AI Infra2026

Indexes mixed-format folders locally into a zero-config, Elasticsearch-compatible search engine offering keyword, semantic, vector and hybrid retrieval. Embeds on ingest, provides per-agent namespaced memory and AST-aware code indexing in a single static binary with no external embedding key.

GitHub

Turns natural-language instructions into end-to-end Android automation that drives real devices and captures Logcat and screenshots. Uses a multimodal “Dynamic‑First, Coordinate‑Fallback” locator and a native MCP server for IDE integration, supporting fast Flash (3–5s/step) and verifiable Pro workflows.