"How to Create a 3D Model Using Blender" matching MCP servers:

Connects Blender to Claude AI through the Model Context Protocol, enabling prompt-assisted 3D modeling, scene creation, and manipulation.

Connects Blender to Claude AI via the Model Context Protocol, enabling natural language-driven 3D modeling, scene creation, and manipulation.

A dynamic service that creates and manages Model Context Protocol (MCP) servers, allowing users to spawn, customize, and control multiple MCP servers as child processes.

Enables AI-driven 3D modeling and scene creation in Blender, allowing LLMs to create, modify, and manipulate objects, materials, and assets through natural language.

Enables AI-driven 3D modeling in Blender by providing tools to create primitives, apply modifiers and materials, set up lighting and cameras, capture viewport snapshots, export assets, inspect scenes, and execute Python commands via natural language.

Enables AI assistants to search many 3D asset sites at once, inspect licences and metadata, and download selected models, materials, textures, HDRIs, or game asset packs directly into a project.

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 assistants to generate 3D product viewers, AR try-on experiences, Shopify integrations, and SEO metadata for e-commerce stores.

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

An MCP server that seats multiple LLMs as a council, letting your assistant query them in parallel or in sequence, relay answers for cross-critique, and merge conclusions within one conversation.

Open-source home design and floor plan editor for AI agents: draw walls and rooms, place furniture, doors and windows, build kitchens and wardrobes, check lighting, ergonomics, electrical and plumbing against building codes, and render the plan, 3D views or photorealistic images. Runs in the desktop app, the browser or the hosted server, and every change can be undone.

Enables interaction with 3D printer management systems through an MCP server, supporting STL file manipulation, slicing, and control of printers like OctoPrint, Klipper, Duet, and more.

Provides AI agents with a toolset to query model inventories, trace dependencies, and analyze the impact of changes across machine learning models and data pipelines.

Enables inspecting ONNX models, probing the deployment environment, and benchmarking inference latency and FPS either locally or on remote SSH targets such as Linux workstations and Jetson devices. Benchmark results are validated against FPS, P95 latency and model-size constraints to produce PASS/FAIL deployment gates.

Enables asynchronous Blender rendering via MCP, supporting Cycles, Eevee, and Workbench engines, with GPU detection and job management for animations and still frames.

Provides verified, up-to-date model IDs, pricing, and specs for over 100 models across 19 providers, preventing AI agents from using outdated or hallucinated model names.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

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.

Routes questions to a council of AI models (local and cloud) and synthesizes their answers in five configurable modes: individual, categorized, deconflicted, pooled, and dialectic.

Enables agents to query governed metrics through MCP, including describing metrics, running deterministic SQL against a warehouse, explaining join path preferences, and searching semantically ambiguous terms.