"Shell" matching MCP servers:

Enables an LLM to design parametric 3D parts in pure NumPy and verify they are actually printable before exporting them. Provides topology checks (watertightness, genus, shell count, wall thickness), self-rendered views, and gated STL/OBJ export that refuses broken solids.

Enables LLM agents to analyze retail shelf photos via computer vision, including product counting, gap detection, price tag reading, and planogram compliance checking.

Gives AI agents vision and control over Android devices using ADB and scrcpy, enabling screenshots, input gestures, app management, UI automation, shell commands, file transfer, and audio/video streaming.

Typed DCC-MCP control for Marmoset Toolbag 4.03+ and 5.x, enabling shell-capable agents to interact with Toolbag scenes via a shared CLI and MCP gateway.

Enables MCP-compatible AI assistants to see and control Android devices through ADB and a fast device-control protocol, including screenshots, touch input, typing, app management, UI inspection, file transfer, shell commands, and audio/video streaming.

Enables AI agents to create layered pixel-art sprites and animations, inspect pixels, and export images, spritesheets, GIFs, APNGs, and TrueType pixel fonts. Documents and edits stay local, accessible over stdio MCP or callable directly from a shell.

Enables MCP-compatible AI assistants to see and control Android devices via ADB and scrcpy, exposing 44 tools for screenshots, taps, swipes, typing, UI element discovery, app management, file transfer, clipboard, shell commands, and audio/video streaming. Uses scrcpy's binary control protocol for fast input and near-instant screenshots, with an ADB fallback when no scrcpy session is active.

Gives MCP-compatible AI assistants vision and control over Android devices — capturing screenshots, tapping, swiping, typing, launching apps, inspecting UI elements, streaming audio/video, transferring files, and running shell commands over ADB, with a fast binary control protocol for near-instant input and screenshots.

Enables shell-less agents to discover available video capabilities, gather timestamped local evidence such as scenes, frames, motion and audio, and query or diff stored observations through MCP tools. Observation and sample directories act as handles, letting hosts and the CLI read the same evidence.

Enables MCP clients such as ChatGPT to execute shell commands, manage files, and view or capture images on Mac and Linux hosts. Supports terminal-based connection approval, time-limited access, and output controls.

Enables AI agents to read and write Elgato Stream Deck desktop profile manifests, generate icons, wrap shell commands in script-backed keys, and restart the app so edits take effect. It runs either locally over stdio or remotely over a bearer-token-gated Streamable HTTP transport that adds a health-check tool.