"Guide to Creating 3D Models in Blender" matching MCP servers:

Create 3D models in Blender using natural language via an MCP server that lets AI agents directly control Blender, enabling complex 3D scene creation through conversation.

Connects Blender to LLMs, enabling prompt-driven 3D modeling, scene creation, and manipulation through natural language.

Enables AI-assisted 3D modeling workflows by connecting Blender with LLMs and CSM.ai, allowing text-based editing, asset retrieval from CSM.ai sessions, and humanoid animation using Mixamo files.

An MCP server that exposes 218 Blender tools for full 3D modeling pipelines, with a LangGraph agent layer for planning, executing, observing, and reviewing modeling tasks with evidence-based verification.

Enables AI-driven 3D model generation and manipulation using OpenSCAD through natural language commands. Users can create primitives, apply transformations, perform boolean operations, and export models to various formats like STL and OBJ.

Enables AI assistants to analyze Blender 3D models for topology issues like n-gons and provide real-time improvement suggestions directly within Blender through an integrated add-on.

MCP server for generating print-ready 3D models from text descriptions via a multi-agent pipeline, enabling AI assistants to create and optimize STL files.

Enables AI agents to control Blender 3D through 27 tools for scene manipulation, materials, modifiers, animation, and rendering, with native Python integration built in Rust.

Enables interactive control of Blender for 3D scene manipulation, geometry creation, material application, and viewport rendering via natural language prompts through the Model Context Protocol.

Unreal-Blender MCP is a unified server for controlling both Blender and Unreal Engine via AI agents using the MCP (Machine Control Protocol) approach.

Enables AI agents to fully control Blender through 50+ tools for 3D modeling, animation, materials, and scene management via HTTP endpoints.

A Model Context Protocol server that allows management and execution of Blender Python scripts, enabling users to create, edit and run scripts in a headless Blender environment through natural language interfaces.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

A guide for implementing Model Context Protocol (MCP) servers that provide AI models with external tools like web search, text manipulation, and mathematical operations.

An MCP server for Blender that exposes a physics-verified optical bench as JSON, enabling AI agents to read ground-truth geometry and beam data, and drive the bench through alignment and corrections.

Orchestrates AI agents through structured markdown documents, enabling multi-agent workflows with automatic context injection and workflow management.

Enables AI coding agents to perform automated Blender UI testing, user-story validation, and computer-use workflows inside an isolated GPU-partitioned Windows 11 VM without disrupting the host desktop.

Enables two local LLMs (Qwen and Llama) to communicate via MCP, allowing one model to consult the other for second opinions or additional reasoning.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.