BlenderMCP: How to Install and Set Up (2026 Guide)

🟢 Beginner–Intermediate ⚙️ Type: MCP Server / 3D AI Assistant 💸 Free & Open Source ⭐ 22,000+ GitHub Stars

What is BlenderMCP?

BlenderMCP is an open-source Model Context Protocol (MCP) server created by developer Sid Ahuja (ahujasid). It acts as a highly intelligent, two-way communication bridge between AI coding agents (like Claude Desktop or Cursor) and Blender, the world’s most popular open-source 3D creation suite.

Historically, if you wanted an AI to help you build a 3D scene, it would generate raw Python scripts that you had to manually copy and paste into Blender’s text editor—often resulting in broken code due to API changes. BlenderMCP fixes this. It exposes a set of reliable, predefined tools that allow the AI to natively interact with your active 3D scene in real time.

By running a lightweight socket server directly inside Blender, your AI assistant can analyze your currently selected objects, read your modifier stacks, spawn new meshes, assign colors, and even automatically fetch and apply professional assets from external libraries like Poly Haven and Sketchfab, all based on your natural language conversational prompts.

Who is it for?

3D Artists and Animators looking to automate tedious workflows, such as renaming hundreds of data blocks or diagnosing why a heavy scene is rendering slowly.
Game Developers wanting to rapidly prototype block-out levels and environmental layouts using natural language before committing to detailed modeling.
Absolute Beginners who find Blender’s notoriously complex user interface overwhelming and want an AI co-pilot to translate their ideas directly into the 3D viewport.
Python Developers who want a safe, isolated pipeline to execute and test generative 3D scripts on the fly.

What makes it special?

Direct Viewport Control — It doesn’t just write code; it executes it. You can tell Claude to “Create a procedural brick wall,” and you will watch the bricks appear and stack dynamically in your Blender window.
Asset Library Integration — It connects the AI directly to external APIs. You can prompt the agent to “Download a wooden texture from Poly Haven and apply it to the floor,” and the MCP server will handle the fetching and node setup autonomously.
Scene Inspection & Telemetry — The AI can actually “see” your project data. It can scan your scene for performance bottlenecks (like an overly subdivided mesh) and even capture viewport screenshots to visually understand the spatial composition of your models.
Hyper3D Rodin Support — It includes integrations for AI 3D model generation, allowing you to prompt a 2D image or text and have the resulting 3D geometry imported directly into your scene.

Requirements before you start

To run this bridge effectively, you need to prepare both your 3D software and your AI environment:

Blender 3.0 or Newer — Ensure your 3D software is up-to-date.
Python 3.10+ — Required for running the background server.
The `uv` Package Manager — An ultra-fast Python package installer required to run the server environment.
An MCP Client — You must have an AI application that supports the Model Context Protocol, such as the Claude Desktop App, Cursor, or Intel AI Playground.

Step-by-step installation

Step 1 — Install the `uv` Package Manager

If you don’t have it already, install uv to manage the Python server. Open your terminal or PowerShell:

Mac/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh
Windows: irm https://astral.sh/uv/install.ps1 | iex

Step 2 — Download the Repository

Clone the BlenderMCP project to a permanent location on your hard drive:

git clone https://github.com/ahujasid/blender-mcp.git
cd blender-mcp

Step 3 — Install the Blender Add-on

The system works in two parts. First, you must install the listener inside Blender:

Inside the downloaded blender-mcp folder, locate the addon.py file.
Open Blender, navigate to Edit > Preferences > Add-ons.
Click Install… and select the addon.py file.
Check the box to enable the add-on. In the 3D Viewport sidebar (press N), you will now see a BlenderMCP tab.

Step 4 — Connect Claude Desktop

You now need to tell your AI client where the server is located. Open your Claude Desktop configuration file:

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Add the following configuration, ensuring you replace the directory with your actual absolute path:

{
  "mcpServers": {
    "blender": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/blender-mcp",
        "run",
        "blender-mcp"
      ]
    }
  }
}

Save the file and restart Claude Desktop. When you chat with Claude, you will see a hammer icon indicating the Blender tools are active. Ask it to “Create a grid of 10 red spheres,” and watch Blender go to work!

Common errors and fixes

Error	What it means	How to fix it
`Connection Refused` when Claude tries to use a tool	The MCP server is running, but the internal Blender socket is closed, so Claude has nowhere to send the commands.	Open Blender, press `N` to open the sidebar, go to the BlenderMCP tab, and ensure you click the Start Server button to begin listening for the AI.
Claude writes Python code in chat but doesn’t execute it	The AI model misunderstood your intent and generated a markdown code block for you to copy, rather than actively calling the execution tool.	Reply to the AI with explicit instructions: “Do not give me the script. Use your Blender MCP tools to execute this code directly in the scene.”
Path parsing errors on Windows	The `claude_desktop_config.json` file contains improperly formatted backslashes in the directory path.	JSON requires escaped backslashes. Ensure your path looks like `C:\\Users\\Name\\blender-mcp` instead of `C:\Users\Name\blender-mcp`.

Free vs Paid comparison

Feature	BlenderMCP (ahujasid)	Promethean AI / Proprietary Assistants
Cost	$0 (Requires your own AI tokens)	Enterprise licensing or heavy monthly fees
Software Target	🟢 Native Blender Integration	Often locked to Maya or Unreal Engine
Extensibility	✅ Open Protocol (Use Claude, ChatGPT, or Local LLMs)	❌ Closed ecosystem
Setup Effort	⚠️ Requires manual config files	🟢 Automated installers

Bottom line: BlenderMCP completely changes how you interact with 3D software. Instead of digging through endless nested menus or reading Python API docs for hours, you can simply converse with your viewport. If you are already using an MCP-capable client like Claude Desktop, setting up this bridge is free and takes less than five minutes. It is an absolute must-have for modern 3D workflows.

Alternatives — 3 similar tools

1. Official Blender MCP Server (by Blender Foundation)

Recently launched by the official Blender development team via the Blender Lab. It shares a very similar goal of providing a natural language interface to Blender’s Python API, with an explicit focus on scene analysis and debugging complex modifiers. It is slightly more official but may have different tool exposures than community-built servers.

🔗 blender.org/lab/mcp-server

2. blender-ai-mcp (by patrykiti)

A more “production-shaped” MCP server. Instead of just exposing raw Python execution (which can be unstable), this repository focuses on bounded, verified workflow tools. It uses goal-first orchestration and assertions to ensure the AI doesn’t break your scene, making it safer for professional pipelines.

🔗 github.com/patrykiti/blender-ai-mcp

3. djeada/blender-mcp-server

Another excellent community-built implementation. It exposes 27 highly specific tools across 7 namespaces, heavily categorized for creating objects, rendering images, exporting scenes, and managing asynchronous jobs without freezing the Blender viewport.

🔗 github.com/djeada/blender-mcp-server

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