You're Already Using All Three Without Knowing It

Last week, a client asked me why they needed an "API" when they were already using Zapier. The week before, another wanted to know what an "MCP server" was after reading an article about Claude. And the question I get most often: "what's the difference between a CLI and an API?"

The reality is that you're already using all three every day. When you type `git push` in your terminal, you're using a CLI. When your Calendly automatically sends a notification to Slack, that's an API call. And when Claude accesses your CRM to find the latest signed leads, that's MCP. Three different mechanisms, three ways to make tools communicate. And since AI agents arrived, understanding the difference is no longer optional — it's become a baseline professional skill.

API: The Universal Language Between Software

An API is how two pieces of software talk to each other without a human clicking anything. Imagine a restaurant: you (the customer) place your order with the waiter (the API), who relays it to the kitchen (the server). You never go into the kitchen yourself. The API is that waiter — an interface that transmits requests and brings back responses.

To make this concrete, take Optimzd, the SaaS I built. The platform connects the Meta Ads API (which sends ad campaign data) to the HubSpot API (which sends signed deal data). A Python script bridges the two to show the real cost per signed client, not per generated lead. Without these APIs, you'd need to export CSVs every morning, cross-reference them manually in a spreadsheet, and pray you don't mix up columns. That's exactly what marketing teams used to do — and exactly the problem APIs solve.

In my daily work, I use about ten different APIs. The Meta Marketing API for ad data, the HubSpot API for CRM, the GA4 Measurement Protocol for server-side tracking, the Google Sheets API for automated reports. Every time, the principle is the same: code sends a structured request to a server, and the server sends back a response. No graphical interface, no clicks, no human in the loop. It's fast, reliable, and runs 24/7.

The important point to remember: an API is not a tool you "use" directly. It's an interface that code uses on your behalf. This distinction becomes crucial for what comes next.

CLI: The Developer's Remote Control

A CLI — Command Line Interface — is a text-based interface where you type commands to control a tool. Instead of navigating menus and clicking buttons, you type what you want and it happens. Think of it as a remote control: instead of pressing physical buttons, you type the action name.

In my daily workflow, the CLI is everywhere. `vercel deploy` to push a site to production in 30 seconds. `git push` to ship code. `npm run build` to compile a project. Each of these commands replaces a series of clicks in a graphical interface — and it's much faster. When I was working on Skello's website, I deployed updates across 7 European markets via the Vercel CLI. One `vercel --prod` and it's live everywhere.

What's changing today is that CLIs are no longer reserved for developers. Claude Code, the tool I use to write code, is itself a CLI. You open a terminal, describe what you want in plain English, and it writes the code for you. It's a CLI powered by natural language. The barrier to entry has never been lower.

The fundamental distinction: a CLI talks to a human. You type a command, you read the response. It's interactive, direct, immediate. An API talks to code. No human sees the requests flowing. Keep this difference in mind — it's at the heart of what makes MCP so different.

MCP: When AI Needs Its Own APIs

MCP — Model Context Protocol — is the most recent and most misunderstood of the three. Launched by Anthropic in late 2024 and since adopted by OpenAI and Google, it's a standardized protocol that lets AI agents discover and use tools automatically.

The fundamental difference is automatic discovery. When you install an MCP server — for example the Vercel MCP server or the HubSpot MCP server — the AI agent does something remarkable at startup: it sends a `tools/list` request to the server, and the server responds with the complete list of everything it can do, with each tool's description and the exact parameter format expected. The agent reads these descriptions and decides on its own which tool to use based on your request.

Concretely, in my Claude Code setup, I have MCP servers connected to Notion, Slack, Gmail, Google Calendar, and Webflow. When I ask "send a message to the #engineering channel," Claude doesn't ask me which tool to use. It knows the Slack MCP server exposes a `slack_send_message` tool, it knows the required parameters, and it calls it directly. I never had to explain how the Slack API works.

With a traditional CLI, the agent must already know the available commands from its training. But there's an important nuance: modern CLIs like Claude Code have developed their own discovery mechanism through skills. A skill is a file that describes a capability — when to trigger it, what to do, how to use it. When you install a skill, Claude Code automatically detects it and knows when to activate it without you explicitly telling it to. It's a form of discovery, but on the CLI side. The difference with MCP: skills are tied to a specific CLI (Claude Code), while MCP is a universal protocol — any AI agent can connect to it.

And to be clear on a point many people confuse: MCP does not replace APIs. An MCP server is built on top of APIs. The Vercel MCP server calls Vercel's REST API under the hood. The Slack MCP server calls Slack's Web API. What MCP does is remove the glue code between the AI agent and the API. The agent no longer needs to know the endpoints, authentication headers, or request format. The MCP server handles all of that.

The Decision Framework: Who's Doing the Work?

The question is never "which concept is better." The question is: who or what is performing the action?

If it's a human doing the work interactively, CLI. You type `vercel deploy`, watch the logs, validate. It's direct, fast, and you have total control. You need to know the command, but once learned, it's the most efficient method for one-off actions.

If it's code doing the work automatically, API. Your CI/CD pipeline calls the Vercel API on every push to the main branch. No human in the loop, it runs on its own. More complex to set up, but once configured, it's reliable and scalable.

If it's an AI agent doing the work, MCP. You say "deploy my site to production" and the agent discovers the Vercel MCP server, picks the right tool, and executes the deployment. You don't need to know the command or the API — the agent handles it.

Let's take a second example with HubSpot and the work I do at Skello. CLI: I type `hubspot contacts list --limit 10` to quickly check recent contacts. API: an n8n workflow calls the HubSpot API every hour to sync leads with Salesforce automatically. MCP: I ask Claude "find all German leads that signed last month" and it queries HubSpot via the MCP server, filters the results, and presents them. Three approaches, same database, three different automation levels.

In practice, you use all three. The skill is knowing which to reach for in which context. Using an API when a simple `git push` would suffice is over-engineering. Doing manually what should be running automatically via an API is wasted time.

What AI Actually Changes in the Equation

Two years ago, these three concepts lived in separate worlds. APIs were for backend developers. CLIs were for devops. And MCP didn't exist yet. Today, AI has blown up those boundaries.

The most visible change: CLIs now speak natural language. Claude Code is a CLI where I describe what I want in plain English, and it writes the code, runs the commands, and deploys the changes. I no longer memorize flags and options — I describe the intent and the tool translates. At Skello, building a HubSpot-to-Salesforce sync required a developer spending 2-3 days writing API integration code. Today, with Claude Code, you can prototype the same thing in an afternoon.

The deeper change: AI agents coexist with both worlds. Claude Code uses CLIs through the terminal — it knows `git`, `npm`, `vercel` from its training. AND it uses MCP tools through the discovery protocol. Both coexist in the same agent. MCP's advantage: discovery is formalized, structured, self-documenting. CLI's advantage: simplicity and universality — any command-line tool works without anyone needing to write an MCP server for it.

And the misconception to avoid: MCP replaces neither APIs nor CLIs. It adds an abstraction layer specifically designed for AI agents. APIs still do the heavy lifting under the hood. CLIs remain the fastest way for a human to execute a one-off action. MCP is the layer that lets an AI agent navigate between these tools without a human holding its hand.

Where to Start Monday Morning

If you work in marketing or ops and all this feels distant, start with CLIs. Install a terminal, type your first command. It can be as simple as `npx create-next-app` to create a site or `vercel deploy` to put it online. The terminal demystifies everything else — once you're comfortable with a text interface, APIs and MCP become much less intimidating.

If you're already comfortable with CLIs, learn an API. The HubSpot API or the Google Sheets API are excellent starting points. Build a small automation with n8n or Python — a script that fetches your leads and sends them to a spreadsheet, for example. You'll understand in 2 hours what all those Zapier integrations you're paying for actually do.

And if you already use APIs daily, explore MCP. Set up Claude with an MCP server for one of your tools (Notion, Slack, your CRM). Watch how the agent discovers the tools and decides on its own which to use. That's the moment the magic happens — when you realize AI isn't just answering your questions anymore, it's acting on your tools.

The goal isn't to become a developer. The goal is to stop being blocked by things you could automate or delegate to an AI agent. API, MCP, CLI — these are the three building blocks. You're already using them without knowing it. Now you know which to reach for first.

If you want to figure out which of these three concepts would unlock the most value in your current stack, let's talk.