"Finding people on LinkedIn using their names" matching MCP servers:

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

Enables ChatGPT's web interface to operate a real Mac by executing shell commands and reading or writing files over a reverse SSH tunnel, without installing a client or consuming API quota.

Gives ChatGPT hands-on control of a local machine: reading, patching and executing code in approved project folders, running real interactive processes, managing plans and worker sub-chats, and driving the browser or desktop through user-granted permissions. Every capability is a switch inside an approved-folder sandbox, so nothing runs that the user did not explicitly enable.

Enables arithmetic operations (add, subtract, multiply, divide) through a custom Model Context Protocol implementation, containerized with Docker for easy deployment.

Enables ChatGPT to work on a local project by reading and editing files, running shell commands, keeping terminals and desktop sessions open, and splitting work across durable workers whose context persists between tasks. Long-running work can be steered mid-flight, tracked with Goal, and carried into fresh chats via Compact & Resume.

Lets ChatGPT work directly on a local project by reading and editing files, running shell commands and tests, keeping terminals open, and using the user's desktop, all paired with an approved workspace folder. It also splits independent jobs across persistent workers that retain context, while riding the user's existing ChatGPT plan rather than Codex quota.

A cross-platform screenshot tool that lets coding agents capture and view their output, enabling self-correction through visual feedback.

Lets Claude manage tmux sessions on Linux — start long-running ML training jobs, check their output, send keystrokes, and kill them.

SSH MCP is a Model Context Protocol tool for managing and interacting with multiple virtual machines over SSH. It simplifies executing commands on remote servers using the standard SSH config file format.

Cross-platform MCP server for policy-controlled command execution on Linux and Windows, with no SSH dependency.

Enables AI assistants to execute shell commands and transfer files via SFTP across remote servers using existing SSH configurations. It supports parallel execution on server groups and provides built-in safety warnings for potentially destructive commands.

Sandboxed Bash for Agents. Full state capture on every command.

Stateful, structured, safe shell sessions for AI agents, on real infrastructure.

An MCP server that reports on and manages systemd services using systemctl and journalctl, enabling service listing, status, logs, and control operations.

A cross-platform MCP server providing secure, user-approved privilege escalation for Cursor agents, with tools for running commands as admin on Windows and root on Linux.

A MCP server that enables human-in-the-loop workflow in AI-assisted development tools by allowing users to run commands, view their output, and provide textual feedback directly to the AI assistant.

Enables AI assistants to manage VMware Fusion virtual machines on macOS using the vmrun CLI, including starting, stopping, and snapshot operations.

Enables AI agents to read tmux pane content on-demand, providing context-aware assistance during interactive terminal sessions.

A secure remote server management tool based on MCP protocol, supporting SSH connections, command execution, and SFTP file transfers.

A local MCP server that lets users control their computer via voice commands, executing CLI tools, searching files, and receiving spoken feedback, with safety gating and confirmation.