MCP servers vs. agent skills vs. CLIs: how to give your AI agent tools

AI agent capabilities can be delivered through MCP servers, skills, or command-line interfaces, with the most suitable option depending on context cost, authentication needs, portability, safety controls, and composability rather than a universally superior format. MCP provides typed tool schemas, remote hosting, OAuth support, and per-tool approval mechanisms, making it well suited to hosted services and sensitive accounts, but its schemas consume context on every turn and calls are less easily chained. Skills use lightweight metadata and on-demand instructions, often to teach agents how to operate existing CLIs, offering broad portability and low context overhead. CLIs provide minimal overhead and strong shell-based composition through pipes, scripts, and parallel operations, though they inherit broad shell permissions and lack built-in structured safety controls. Examples from Hermes, Pi, and OpenClaw illustrate that GitHub and other local or composable tools often work better as skills wrapping CLIs, while MCP is most useful for authenticated remote services. The recommended approach is to select MCP for hosted, stateful, authenticated tools, skills paired with CLIs for occasional or local workflows, and bare CLIs for shell-first agents with straightforward commands.

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