"How to automate workflows in DaVinci Resolve and Fusion" matching MCP servers:

A Model Context Protocol server for ConnectWise Automate with decision tree architecture, enabling Claude to manage scripts, computers, groups, and automation workflows.

Enables AI agents to control and interact with a local Windows computer through authenticated MCP tools, including file operations, shell commands, process management, HTTP requests, and system/media inspection.

Enables AI agents to securely connect to and manage remote servers via SSH, with tools for running commands, file operations, log inspection, service management, and system monitoring under configurable access controls and audit logging.

A local MCP runtime for macOS and Windows that enables ChatGPT to read, edit, search, run tests, and capture screenshots in your local project folder while keeping your source code on your machine.

Enables a Claude Code cloud session to run shell commands inside a chroot jail on your own computer and to call the MCP servers already installed there, with every request authenticated and relayed over a Cloudflare quick tunnel so no router port is opened and no SSH key or tunnel is ever handed to the cloud VM.

Enables AI clients to securely execute remote commands and transfer files over SSH through standardized MCP tools, with multi-host management, flexible authentication, and an admin console.

A simple MCP server that exposes a terminal tool, allowing AI agents to execute shell commands.

The m2m-mcp-server-ssh-client provides a unified MCP server interface, securely connecting via SSH to the m2m-mcp-server-ssh-server back-end which aggregates MCP servers. This setup offers secure, single-point access to diverse remote tools using robust SSH key-based authentication.

An MCP server that enables AI assistants to execute shell commands and manage files on remote servers via SSH. It supports recursive directory uploads, archive extraction, and direct file creation from text content.

Enables AI assistants to fully interact with console applications, monitor output, detect errors, and automate terminal workflows across multiple sessions. Similar to how Playwright works for web browsers but for command-line interfaces.

Enables MCP-compatible AI clients to connect to a Windows machine for file operations, PowerShell execution, Git inspection, task management, memory, and skills.

A production-ready MCP server that enables AI assistants to execute shell commands, manage files, monitor system resources, and automate complex workflows with advanced features like stock tracking and web automation.

An MCP server that connects Claude Desktop to an interactive Claude Code session running in a tmux terminal.

A FastMCP server that enables AI agents to safely execute shell commands, run code in Python/Node.js/Go, and install packages, all packaged in Docker for secure deployment.

An MCP server that enables agents to run commands interactively in live terminal panes a user can watch and type in, and to list, create, read from, and send keys to any session, window, or pane.

Local Dev Doctor turns an unfamiliar development checkout into an evidence-backed diagnosis. It discovers how the project is meant to run, checks safe prerequisites, and executes only the exact probe that a user approves.

Enables AI agents to ask users for credentials, secrets, and sensitive data through local browser forms that the model never sees, then resolve and use those values only at approved points of use. Also supports blind web capture, SSH key provisioning, and writes to .env files, SQL databases, and APIs, with the agent receiving only structural results.

Provides a governed command plane for MCP agents to manage PostgreSQL databases, SSH sessions, and HTTP APIs with encrypted credential storage and prescriptive workflows.

SSH Session MCP fills a gap in the MCP ecosystem by offering a persistent shared SSH PTY runtime, not just stateless command execution. It features browser collaboration, input locking, safe/full execution modes, async command tracking, configurable policy rules, and multi-device profiles. Ideal for remote development, embedded systems, infrastructure workflows, and hardware control scenarios.

MacOS MCP server to discover, control, automate, and inspect Parallels virtual machines over standard MCP stdio. Requires Parallels Pro or Business Edition subscription.

Enables AI assistants to inspect, control, and automate Arch Linux/CachyOS and the Niri Desktop Environment through natural language, featuring tools for window management, system administration, package queries, and hardware control.

Enables MCP agents to automate real GUI applications on headless desktops, providing background mouse/keyboard control, window/process management, screenshots, and safe human handoff without disturbing the user's desktop.

MCP server to control Onda terminal from AI agents, providing tools for splitting panes, running commands, managing tabs and workspaces, and orchestrating multi-agent workflows across multiple windows.

A Model Context Protocol server that enables Claude Desktop to execute bash commands and scripts in WSL2 Linux distributions on Windows.

MCP server for controlling VMware Fusion/Workstation virtual machines via vmrun CLI, providing 32 tools for VM lifecycle, guest execution, and file operations without requiring a REST API daemon.

Enables AI agents to create and control persistent terminal sessions, run and monitor asynchronous jobs, page through ordered event history, and resolve registered device connection details without exposing stored secrets. Exposes 15 MCP tools over stdio that mirror a REST API, keeping session state alive independently of the agent that started it.

A secure server that enables AI applications to execute shell commands in specified directories, supporting multiple shell types (bash, sh, cmd, powershell) with built-in security features like directory isolation and timeout control.

Enables AI agents to execute and interact with terminal commands through MCP, supporting command-line operations and workflows.

Enables AI agents and LLM workflows to run common CLI tools like kubectl, terraform, helm, git, and jq while receiving structured JSON with typed schemas instead of raw human-readable output.

Enables execution and management of Babashka scripts using the Model Context Protocol, offering features like caching, command history access, and configurable timeouts for enhanced scripting workflows.