Almost Timely News: 🗞️ Improving AI With Command Line Tools (2026-04-12)

Last week I talked about the command line interface and how useful it was with today’s agentic AI tools, and I gave three examples. Many of you wrote back to say “we’d like to see more examples”. So this issue is your wishes fulfilled.

Part 1: Why the Command Line Matters

For years now, we’ve talked about how things like MCPs – model context protocol servers – were the way to connect AI to external systems. That’s true to a great degree, but it turns out MCP implementations are horrendously inefficient.

When you use an MCP to connect to a database or a web service, you’re telling your favorite AI tool how to talk to it and interact with it – pulling and pushing data. If this sounds familiar, it should. MCPs are to AI what APIs – application programming interfaces- are to regular software.

The difference is, in many, many measured tests, AI tools and agents use MCPs incredibly inefficiently. They do a lot of reinventing the wheel every time they interact with an MCP. For casual use where you’re typing into a tool like Claude or ChatGPT, that doesn’t matter a ton. But once you start using AI agents, tools like Claude Code, Claude Cowork, OpenWork, OpenCode, Qwen Code, OpenClaw, etc., efficiency becomes very important.

The reason why is that AI agents produce far more inputs and outputs than humans do. To give you a sense of scale, if you spend half an hour chatting with a tool like ChatGPT, you’ll probably generate somewhere in the range of 15-20 thousand words, about the length of a short business book. An agent like Google Antigravity or Claude Cowork, in that same half hour, will generate 15-20 MILLION words, three orders of magnitude greater.

Why? Partly because AI tools can read far faster than any human can, and partly because agent tools aren’t sequential. When you use ChatGPT, it’s you and one conversation thread. When you’re using something like Claude Cowork, behind the scenes there can be 50 different agents working in 50 different conversations at the same time. My record was having 120 agents all in flight at the same time, all simultaneously. (It was writing and editing a book, so each chapter had 4 agents working together – a writer, an editor, a reviewer, and a proofer)

If an MCP is involved, AI agents are reinventing the wheel every time they interact with it. That makes it very slow, introduces the potential for lots of randomness and lots of mistakes, and when you’re using something like an API where you pay by the word, agents using MCPs can run up massive bills very quickly.

In the early days of OpenClaw, early adopters weren’t watching the meter and more than a few got ten thousand dollar bills from companies like Anthropic. Oops.

The alternative to the MCP is the command line interface, or CLI. If you’re old like me, you used to use these when you interacted with computers. Back in the days of the Apple IIe and VisiCalc, AppleWorks, Lotus 1-2-3, and MS-DOS, there was no graphical user interface (GUI). There was no mouse. There was you and the text command line, and every program you interacted with had text-based commands.

Perhaps you’ve heard of Zork or similar role-playing games where you’d type things like direction names, or instruct your character to open a treasure chest, and the game would tell you in words what was happening:

You have entered a dank, dark dungeon-like room. There is a desk in the corner. On the desk is a laptop open to someone’s Google Analytics account. What do you do? Press 1 to look at their analytics. Press 2 to switch to Google Tag Manager. Press 3 to laugh loudly and mock their lack of UTM tagging. Press 4 to leave the room.

These text-based apps were all the rage until the Macintosh computer in 1984 (well, technically the Xerox PARC computer had the mouse and GUI first, but Apple commercialized it first) when we got the mouse and GUI. But the command line interface never went away. It’s been present in every computer since the dawn of computing. On a Windows computer, it’s the Command Prompt, CMD. On a Mac and Linux, it’s the Terminal.

And operating systems themselves on computers have many, many little applications built into the command line, simple pieces of software that do one thing, but do it well. This was the original architecture of computer systems like UNIX, because memory was so scarce that it was better to have a tool that did one thing very well than a tool that tried to do many things and did them all poorly.

For example, on the Mac and Linux systems, there’s an app called wc. wc counts things – letters, words, and bytes. Give it a text document like this:

wc -w input.txt

And it will tell you how many words are in that document. That’s all it does. That’s all it needs to do.

Hopefully you see where this is going. Who loves to type and is great at typing at ridiculous speeds?

AI.

Instead of a slow, cumbersome, error-prone MCP, a command line application (also known as a CLI) provides a deterministic (non-random) way to access the same services that provide MCPs. But here’s the twist: in testing, many folks have found that AI agents burn through 2/3 less usage using a CLI as it does an MCP.

If you’re on Anthropic’s Claude subscription plans, you’ve no doubt hit usage limits. If you’re using APIs, you’ve no doubt gotten bills from AI providers. What if you could cut those bills down significantly?

With CLIs instead of MCPs, you can do exactly that.

Part 2: Switching Over

There’s a gotcha with CLIs. You have to tell AI agents how to use them. Depending on the system you’re working with, your AI agent may need to install it themselves (this is often the case in container-based systems like Claude Cowork, which run in a sandbox on your computer, isolated from the rest of your computer), or your AI agent may be able to access it directly (Claude Code, OpenClaw) which is more risky because the agent is working directly on your computer.

That’s the first hurdle to overcome. The easiest way to overcome it is to ask your AI tool of choice how it can access CLIs, what it needs to do.

I should point out here that NONE of the web-based tools (like ChatGPT, for example) can use CLIs at all, so MCPs are still the only way for those web-based tools to access third party services.

What AI tools can use CLIs? Anything agentic:

Coding tools: OpenAI Codex, Google Antigravity, Claude Code, Gemini CLI, Qwen Code, OpenCode
Coworking tools: Claude Cowork, OpenWork, et. Al.
Agentic frameworks: OpenClaw, MyClaw, NemoClaw, etc.

It’s also worth pointing out that today, I use a coding environment (Claude Code and OpenCode) for almost all my AI usage including many, many non-coding tasks. I rarely, if ever, use the web-based chats any more. More on that in a second.

Your second step, after making sure you’ve got AI that can use CLIs, is to determine what software you’d want to use a CLI with. Think of it this way: what software do you use every day that you wish you could have AI use on your behalf? What about GMail or Outlook? Microsoft Word? Google Sheets? NotebookLM? Your CRM?

Once you’ve made a list of the things you wish you could hand off to AI in some capacity, it’s time to do a bit of Googling (or have an AI agent do it). Google for this:

{vendor} CLI tool Github

For example:

Joplin CLI tool GitHub

This will do a search to see if there’s a CLI for my notetaking app, Joplin. And there is! Now I don’t have to worry about APIs or MCPs or all kinds of contortionism to get AI to talk to my note-taking tool. Instead, I install the CLI and now there’s a text-based way for AI to interact with it.

Not all software packages have CLI tools. As a sidebar, if you work for a software company and you don’t offer a CLI, you’re almost obsolete. As more and more people use AI agents, the demand for interfaces like CLIs will become greater, and companies that fail to offer first class interfaces to AI will inevitably experience a lot of churn.

Okay, you’ve figured out that your favorite software has a CLI. Install it, and then what? Great question. We have to tell AI that it’s available and how to use it. There’s an easy way to do this; almost every CLI tool has a documentation page of some kind. We point our AI tool at that page and ask it to make its own user guide. Here’s a suggested prompt:

Make a concise guide for how to use {tool} based on the documentation at {documentation URL}. This tool will be installed locally and will be used by both a human and an LLM with tool handling, invoked by the command shell. Your concise guide should be in strict YAML format with the commands, command flags, a concise single sentence explanation of the command, a concise single sentence explanation of the use cases or when to use the command, and a concise single sentence explanation of when not to use the command (antipatterns). Prioritize lexical compression and lexical density to achieve conciseness. Write in second person imperative in active voice only.

What this will produce is a condensed version of the manual and documentation ready for AI to use. You can do this right inside your agentic system like Claude Cowork and have it write the YAML file to your hard drive.

Then, depending on you have your agentic system set up, you can give it brief instructions when you want to invoke the CLI, or better yet, incorporate it into skills and hooks so that your AI agent automatically knows when to use it and how. We talked a little about this on the Trust Insights Livestream this past week.

It’s also worth pointing out that one advantage CLIs have over MCPs is that they’re actual applications. If you’re old like me, you remember using apps back in the day exactly like this, and you can pick up and use these CLIs as apps, as a human. For example, suppose you’re using Atlassian’s Jira, but you find the website cumbersome to navigate. You can read the same manual that AI wrote for itself and use Jira in a Terminal just like your AI does. This is not for everyone, especially since younger folks have never had computers without GUIs, but depending on how your brain works, you might even prefer a CLI to a GUI app. If you’re easily distracted, CLIs can be a great way to single task and focus. If you or someone you know is low-vision, CLIs are AMAZING.

Part 3: Demo Walkthrough

In the video version of the newsletter, you can watch me set this up using Joplin’s CLI step by step to see the flow.

Part 4: A Compendium of Common CLIs

Now that you’ve got the general idea about how to use these CLIs, let’s look at a rogues’ gallery of some of the most common ones, with links so you can go find them.

I would strongly, strongly encourage you to use a package manager for CLI tools when possible, because it makes keeping them up to date much easier. On the Mac, this is Homebrew. On Windows, this is Chocolatey. On Linux, it’s apt or yum or deb or… well, there’s a gazillion package managers, honestly. Use the one that’s best for your distribution. Read up on how to install the appropriate package manager, if you’re allowed to (some corporate IT departments frown on systems like this because it makes device management very difficult).

Some software companies have published official CLIs. Others have not, but because their software has APIs, folks have made unofficial versions. In general, prefer official CLIs over unofficial ones to minimize security and privacy risks. If you have to use an unofficial one and it’s on Github, you can ask your agentic coding tool of choice to audit it for safety, security, and privacy risks before installing it. This is a generally good best practice to get in the habit of and one of the primary reasons to use open source software – you can audit the software itself.

The list of CLIs is massive – 135 of them, so I’ll highlight a few here and put the rest as an appendix at the end of the newsletter.

Cloud Platforms & Infrastructure

AI & ML Platforms

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CRM & Sales

Part 5: Wrapping Up

Command line interface tools are the best way for AI to interact with non-AI systems today. They’re deterministic – AI has mandated ways to interact with the tools and inputs and outputs are rigid. They’re incredibly efficient – unlike browsing a web site or managing a virtual keyboard and mouse, typing out commands is low bandwidth and low effort for AI and humans. They don’t consume tokens/AI usage getting data in and out of the target systems because the CLI does that without AI; it’s a black box from AI’s perspective.

Your homework is to follow the process in this newsletter with the CLI of one of the tools you use, handing it to a system like Claude Code or Claude Cowork, and asking it to help you get set up to use that CLI in your work. You may be surprised to find out just how much AI can take on.

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Appendix: CLI Apps

Cloud Platforms & Infrastructure

DevOps & CI/CD

Databases

AI & ML Platforms

Containerization & Orchestration

Monitoring & Observability

Security & Identity

Communication & Chat

Project Management

E-Commerce

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What’s On My Mind: Improving AI With Command Line Tools

Part 1: Why the Command Line Matters

Part 2: Switching Over

Part 3: Demo Walkthrough

Part 4: A Compendium of Common CLIs

Cloud Platforms & Infrastructure

AI & ML Platforms

Communication & Chat

CRM & Sales

Part 5: Wrapping Up

How Was This Issue?

Share With a Friend or Colleague

ICYMI: In Case You Missed It

On The Tubes

Skill Up With Classes

Premium

Free

Advertisement: New GEO 101 Course

Get Back To Work!

Advertisement: My AI Book!

How to Stay in Touch

Advertisement: Ukraine 🇺🇦 Humanitarian Fund

Events I’ll Be At

Required Disclosures

Thank You

Appendix: CLI Apps

Cloud Platforms & Infrastructure

DevOps & CI/CD

Databases

AI & ML Platforms

Containerization & Orchestration

Monitoring & Observability

Security & Identity

Communication & Chat

Project Management

E-Commerce

Accounting & Finance

CMS & Website

Social Media

Cloud Storage

Version Control

Networking & VPN

Data Orchestration

Developer Productivity

Document & Knowledge

CRM & Sales

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