ChatGPT Machine MCP
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- AlicenseAqualityBmaintenanceEnables ChatGPT web to use local tools like file reading, command execution, and patch application through an MCP server over OpenAI Secure MCP Tunnel.62MIT
- FlicenseCqualityDmaintenanceEnables ChatGPT to control a Windows PC remotely via OpenAI Secure MCP Tunnel, executing file operations, PowerShell commands, and system actions through a local MCP server.15-

Flyto2 Runtimeofficial
AlicenseNot gradedqualityBmaintenanceGives ChatGPT secure access to your local machine over MCP, letting it read and edit files, run commands, tests, and Git within approved workspaces.1MIT- AlicenseNot gradedqualityDmaintenanceEnables ChatGPT to securely control a local workstation via an MCP tunnel, exposing 44 tools for file/project editing, git, process supervision, browser automation, and Office document handling across macOS, Linux, and Windows.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables ChatGPT to access a remote computer or server via an outbound-only agent, providing filesystem inspection, file editing, Git inspection, and optional shell execution through MCP.MIT
- AlicenseNot gradedqualityCmaintenanceEnables ChatGPT Web to discover and invoke native local Codex tools via OpenAI's Secure MCP Tunnel, with the local host executing commands and returning results.3MIT
TDQS
Scored across 62 tools
Several tool clusters have unclear boundaries: edit_file, update_file, write_file, and apply_patch all mutate files; machine_status and runtime_info overlap as health/status checks; git_commit and git_commit_verified are easy to confuse. The descriptions help individually, but the sheer number of similar-purpose tools makes misselection likely.
Most tools follow a clean snake_case verb_noun pattern (read_file, write_file, start_process, git_status, list_directory), and there is a recognizable 'info' suffix group. Minor deviations like runtime_info, capability_diff, self_update, and git_commit_verified break the pattern slightly but are not chaotic.
With 62 tools, this server is far beyond a well-scoped MCP surface. Even for a broad machine-management and coding-agent server, the count is overwhelming and well into the extreme-mismatch range.
The toolset is very comprehensive for its apparent domain: file operations, process lifecycle, Git workflows, learning/todo persistence, remote machine invocation, audit, and self-update are all covered. Minor gaps like a dedicated delete_file tool or remote machine registration/removal are present, but agents can work around them (e.g., apply_patch can delete files).