"A guide to creating 3D models in Blender" matching MCP servers:

Turns a single image of an object into a textured 3D model (GLB with web-ready copy and six-view previews) on your own NVIDIA GPU, returning the written files and a preview. Runs locally as an MCP tool or, optionally, offloads the job to hosted GPUs with an API key.

Enables searching, comparing, and inspecting AI models by pricing, context window, and capabilities via the models.dev catalog.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Enables AI agents to access and manage project guidelines, documentation, and context through a structured content system with template support and workflow management.

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

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.

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 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.

Enables AI agents and LLM platforms to create and manage live 3D isometric sessions, upsert agents, update progress and status, trigger speech bubbles, and remove agents through stateful MCP tools over STDIO or HTTP.

Enables AI agents to look up facts about notable machine-learning models — organization, release date, parameter count, training-compute (FLOP) estimate, hardware and accessibility — by name, and to search or aggregate them by organization, domain, year, or compute threshold. Supports filtering the dataset newest-first and producing per-year training-compute trends, keylessly and without authentication.

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.

Enables users to choose among seven decision-making frameworks and work through them step by step with their own situation, ending with one clear decision and a next action. It also chains multiple frameworks for larger decisions and provides model guides and recommendations.

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.

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.

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

Enables AI tools to understand a database, inspect schema, and run safe SELECT queries with SQL guardrails, plus optional codebase reading.

Persistent memory and pattern learning for AI coding assistants via MCP.

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 control a live Blender session for scene/object manipulation, rendering, code execution, and PolyHaven asset/texture management. It also supports runtime switching among Ollama, LM Studio, llama.cpp, OpenAI-compatible, and Azure backends.

Enables AI agents to control a live Blender session through scene, object, rendering, and PolyHaven asset tools while sending prompts to a provider-agnostic LLM backend that can be switched at runtime.

Lets AI agents drive a live Blender session — reading scene and object data, creating, modifying and deleting objects, setting materials, rendering images, executing code, and downloading PolyHaven assets. Prompts flow through a swappable, provider-agnostic LLM backend (Ollama, LM Studio, llama.cpp, OpenAI-compatible, Azure) that can be reconfigured at runtime without restarting.

Enables users to describe an LLM fine-tuning job once and receive VRAM/FLOPs-aware recommendations for the cheapest, fastest, or most balanced GPU options across a dozen cloud providers.

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

Enables AI agents to control a live Blender session over MCP, creating and modifying objects, applying materials, rendering images, and downloading PolyHaven assets. It also routes prompts to a provider-agnostic LLM backend (Ollama, LM Studio, llama.cpp, OpenAI-compatible, Azure) that can be listed and switched at runtime.

Enables AI agents to control a live Blender session through MCP tools for scene and object operations, rendering, PolyHaven asset handling, and LLM prompting. Supports multiple configurable and runtime-switchable provider backends, including Ollama, LM Studio, llama.cpp, OpenAI-compatible endpoints, and Azure.

Enables AI agents to control a live Blender session for scene inspection, object creation/modification, rendering, PolyHaven asset handling, and LLM-assisted prompts. It supports provider-agnostic backends such as Ollama, LM Studio, llama.cpp, OpenAI-compatible endpoints, and Azure, with runtime switching via MCP tools.

Enables AI agents to control a live Blender session through Model Context Protocol tools for scene and object manipulation, rendering, PolyHaven asset handling, and runtime-switchable LLM backends. It supports local and OpenAI-compatible providers, allowing prompts and provider configuration changes without restarting the server.