"Information on ordering products from websites" 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.

Connects ChatGPT to local files, terminals, and the desktop through MCP with Chrome integration, letting users read and edit project files, run commands, and follow tool results as they arrive. It also adds durable multi-agent workers, Goal/Loop task tracking, mid-task corrections, and Compact & Resume for carrying session and worker history into a fresh chat.

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 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.

Enables AI assistants to understand lab topologies, follow operational principles, and interact with real network devices through observable terminals while keeping private access information hidden.

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

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

Runs git-bash (MSYS2) commands for AI agents on Windows from outside the agent's sandbox, so pipelines, command substitution, and subprocesses work. Exposes exec, bash_info, doctor, and policy tools with timeouts, output spilling, concurrency limits, capability-based command gating, and JSONL auditing.

A minimal MCP server that exposes a single tool to query local hardware information (OS, CPU, memory, architecture).

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 intelligently filters and compresses tool outputs to reduce context window usage, saving up to 90% of tokens by removing noise such as passing tests and redundant information.

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.

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.

Enables executing commands and transferring files over SSH on multiple hosts concurrently, with auto-discovery from SSH config and support for various authentication methods.

Lets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.