Enables AI agents to programmatically edit videos via FFmpeg, providing tools for probing, trimming, concatenating, timeline composition, LUT generation/application, rendering, and edit verification over MCP.
Enables AI agents to drive a user's own signed-in Google NotebookLM (Gemini Notebook) account through 51 MCP tools, covering notebook and source management, grounded chat with numbered citations, fast and deep research, and generating or downloading audio/video overviews, slide decks, quizzes, flashcards, mind maps, infographics, reports and data tables. It runs over stdio or streamable HTTP on localhost and sends progress notifications for long-running jobs.
Enables small models to turn a short brief into complete HTML pages or looping GIFs with typography, palette, borders, motion, and validation already decided. It also lets agents list styles, retrieve style rules, create assets, validate HTML, and access reference examples.
Enables AI assistants to control DaVinci Resolve (Free and Studio) through natural language, providing 162 tools for timeline editing, color grading, rendering, and more. Includes local AI replacements for Studio-only features like voice isolation, background removal, and transcription.
Enables agents to commission and refine web pages, product prototypes, landing pages, decks, images, and motion through the desktop app, and to read design files, copy artifacts, and access design systems.
A zero-cost, China-accessible MCP server providing four free AI capabilities: text chat, image generation, speech-to-text, and text-to-speech, ready to use after cloning.
Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.
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 MCP clients to discover OpenRouter image and video models, generate and edit images, create variations, launch and monitor video jobs, and save resulting media into authorized local project directories with cost controls.
Enables recognition and extraction of architectural CAD drawings (DWG) by creating tasks and retrieving structured results for frames, title blocks, and 23 building component types, including rooms, doors, windows, stairs, and text.
Enables AI coding agents to perform end-to-end medical image analysis locally, from inspecting CT/MRI/X-ray studies and running containerized segmentation models like MedSAM2, TotalSegmentator, and HD-BET to refining masks, measuring volumes, and writing reports. It pairs preset or custom guideline protocols with a code-customizable viewer that renders images back to agents as annotated PNGs and to humans as interactive HTML.
Exposes Open Video Guide functions to MCP clients, enabling conversion of tutorial videos into illustrated, verifiable guides with timestamps, evidence, and review states.
A server that integrates Blender with local AI models via the Model Context Protocol, allowing users to control Blender using natural language prompts for 3D modeling tasks.
Enables AI agents to control a live Blender session, including scene and object operations, materials, rendering, PolyHaven asset downloads, and prompts to configurable LLM backends through MCP tools.
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 and use configurable LLM backends through MCP tools for scene/object editing, rendering, asset search/download, and runtime provider switching.
Enables AI agents to drive a live Blender session over MCP by inspecting scenes, creating and modifying objects, assigning materials, rendering images, and fetching PolyHaven assets. It also exposes prompt-based scene building and review workflows backed by a provider-agnostic LLM layer (Ollama, LM Studio, llama.cpp, OpenAI-compatible endpoints, Azure) that can be switched at runtime.
Enables AI agents to control a live Blender session for scene and object creation, modification, rendering, and PolyHaven asset workflows, while routing prompts through configurable local or remote LLM backends that can be switched at runtime.
Enables AI agents to control a live Blender session, manage scene objects and materials, render images, fetch PolyHaven assets, and send prompts to configurable local or OpenAI-compatible LLM providers with runtime switching.
Enables AI agents to control a live Blender session through the Model Context Protocol, exposing tools for scene and object management, materials, rendering, and PolyHaven asset downloads. It also lets agents send prompts to a swappable LLM backend (Ollama, LM Studio, llama.cpp, OpenAI-compatible, or Azure) and switch providers at runtime.
Enables AI agents to inspect and modify live Blender scenes, manage objects and materials, render images, retrieve PolyHaven assets, and send prompts to configurable local or OpenAI-compatible LLM backends.
Enables AI agents to control a live Blender session through MCP tools for scene/object manipulation, rendering, PolyHaven asset access, and guided scene-building prompts. It supports provider-agnostic LLM backends that can be configured or switched at runtime.
Enables AI agents to control a live Blender session through the Model Context Protocol, including scene and object operations, rendering, PolyHaven asset integration, and prompt-driven workflows. It supports multiple local or OpenAI-compatible LLM backends that can be switched at runtime.
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.
Enables AI agents to drive a live Blender session — inspecting scenes, creating, modifying and deleting objects, setting materials, rendering images, and searching or downloading PolyHaven assets and textures. It also routes prompts to a pluggable LLM backend (Ollama, LM Studio, llama.cpp, OpenAI-compatible, Azure) that can be listed and switched at runtime without restarting.
Enables MCP clients to generate images through multiple free/low-cost providers with automatic failover, plus session history, provider status, and configuration tools.
Enables AI agents to control a live Blender session, creating, modifying, and deleting objects, applying materials, rendering images, executing code, and pulling PolyHaven assets, while routing prompts through a provider-agnostic LLM backend that can be switched at runtime.
Enables AI agents to control a live Blender session for scene and object creation, modification, rendering, and PolyHaven asset workflows, while routing prompts through configurable local or remote LLM backends with runtime provider switching.
Enables AI agents to control a live Blender session through the Model Context Protocol, including scene and object manipulation, rendering, PolyHaven asset access, and runtime switching between multiple LLM backends.
Privacy-first MCP server for macOS that allows AI agents to search local images using natural language descriptions, leveraging MLX CLIP embeddings and LanceDB for fast, fully offline search.
Enables natural language search of local photo archives using AI-powered semantic understanding, with integration into Claude Desktop via the Model Context Protocol.
An MCP server that searches the web for images, creates a numbered contact sheet for visual selection, and downloads the chosen images with provenance metadata. Works without an API key.
Enables AI agents to inspect image metadata, evaluate print viability, and autonomously upscale images to 8K or 16K using RealESRGAN with GPU acceleration and CPU fallback.
