"A 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.

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 an Alexa+ host to place and manage customer-care calls by building a case, navigating phone menus, waiting on hold, explaining the issue, guarding sensitive disclosures, handing off identity checks, recording ticket numbers and promises, and scheduling follow-ups.

Enables AI agents to drive a live Blender session over stdio, inspecting the scene graph, searching and calling operators, reading and writing RNA properties, rendering viewable images, and checkpointing or restoring the file across a multi-turn conversation. It also exposes gallery and scene-reset helpers so the agent can check its own work and start over cleanly.

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.

Enables AI agents to call tools through a security gateway that intercepts requests, enforces deterministic policy and risk checks, blocks or escalates high-risk actions for human approval, and keeps tool credentials and audit logs protected.

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.

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.

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.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

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

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.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

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.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Enables AI agents to control and manipulate live 3D scenes across frameworks like Three.js, A-Frame, and Babylon.js using a comprehensive set of object and environment tools. It features an integrated in-world chat system that allows for real-time scene modifications directly from within the 3D canvas.

Enables AI assistants in Google Antigravity to drive Blender for 2D-image-to-3D reconstruction, running a multi-stage pipeline to analyze reference images, generate models, compare renders, refine geometry, and produce reports.

A Model Context Protocol server that converts Markdown content to HTML format.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.