API, MCP, CLI, and Skill: What's Different, and When to Use Each
REST API, MCP server, CLI, Claude Skill. If you have been experimenting with AI tooling lately, you have run into all of these. You’ve probably heard of each – but the question "so which one should our team use?" is harder to answer clearly than it should be.
For teams trying to bring AI into their organization, the same questions keep coming up:
"Don't we just need to plug in MCP and we're done?"
"We already have an API – can't we just use that?"
"This is the agent-first era, right? So everything has to be MCP?"
"And Skill is... a different name for MCP?"
A clear, side-by-side breakdown of these four tools – similar enough to be confused, different enough to matter – is surprisingly hard to find. This article covers it all: what each one is, how they differ, and when to use which.
The four tools
Before any comparison, let’s get the vocabulary straight. All four are interfaces that let an AI agent work with external systems. What separates them comes down to three things: when they emerge, how they work, and who calls them.
API (Application Programming Interface)
The most familiar interface is a good starting point. An API is a channel through which two systems communicate in an agreed-upon format – usually JSON over HTTP. It has been the backbone of the web since the 1990s, and it still sits underneath every SaaS product today.
# A Stripe API call to create a charge
POST https://api.stripe.com/v1/charges
Authorization: Bearer sk_live_...
Content-Type: application/x-www-form-urlencoded
amount=2000¤cy=usd&source=tok_visaAn API doesn’t care who’s calling. Whether it’s a human hitting it with curl from a terminal, a backend service invoking it, or an AI agent calling it, the response is the same. That is why the API is the foundation of the other three tools. Peel back MCP, CLI, or Skill, and underneath, you’re always calling an API.
MCP (Model Context Protocol)
An open standard released by Anthropic in November 2024. A protocol that lets an AI agent communicate with external tools and data in a consistent format. It runs on top of JSON-RPC.
Concrete example: In Claude Desktop, you ask: "Show me my GitHub PRs." For Claude to answer, it needs to reach GitHub. Claude can’t memorize GitHub's entire API on the fly, so GitHub publishes a description ("here is how to call my tools") through an MCP server. Claude reads that schema and calls the matching tool.
// Simplified view of an MCP server introducing itself to Claude
{
"tools": [
{
"name": "list_pull_requests",
"description": "Lists pull requests in a repository",
"inputSchema": {
"type": "object",
"properties": {
"owner": { "type": "string" },
"repo": { "type": "string" },
"state": { "enum": ["open", "closed", "all"] }
}
}
}
]
}The power of this structure is standardization. Claude Desktop, Cursor, Claude Code – any host that speaks MCP can plug into any company's MCP server the same way. By 2025, major SaaS players – GitHub, Notion, Figma, Slack, Stripe, and Linear – had all released official MCP servers in quick succession.
CLI (Command Line Interface)
Tools you type commands into directly in a terminal. git push, gh pr list, aws s3 ls, npm install. They have been around since the Unix era of the 1970s, and developers use them everyday.
# GitHub CLI listing PRs
gh pr list --state open --limit 5
# AWS CLI listing files in an S3 bucket
aws s3 ls s3://my-bucket/data/
# Stripe CLI listing recent charges
stripe charges list --limit 3Then this decades-old tool became a main character of the AI era. The reason is simple: AI assistants can use it too. Claude Code, Cursor, Codex, and other assistants are given permission to type commands directly into a computer. Instead of a human typing the command, the AI does.
Which leads to situations like this: say you want to "show me the code changes pending review." Previously, this required a separate connector (an MCP server) to wire the AI through several steps. With a CLI, the AI just types one line (gh pr list) and it’s done. No bridge needed.
Skill (Claude Skills)
The newest of the four. A Skill is procedural knowledge written in a markdown file – a guide that says "to do this task, follow this sequence, in this order." Skills don’t fetch data or take actions. They only define the sequence and the rhythm.
---
name: weekly-meeting-report
description: Use when extracting decisions from the week's meetings to build a report
---
## Procedure
1. Pull this week's meetings via the tiro CLI
tiro notes search "OKR" --since 7d --json > meetings.jsonl
2. Extract only the "decisions" section from each meeting
3. Fill the following template into a markdown report
4. Post to the #weekly-report Slack channelIn other words, Skill is not a replacement for MCP – it’s a complement. MCP provides tool access. Skill tells you the procedure for using those tools effectively.
Same question, 32x cost difference
With definitions in place, here’s the core question: does the same task produce similar results across these tools? Not even close. The data tells a surprising story.
A March 2026 benchmark from Scalekit starts with a very simple question. "What language is this GitHub repo written in?" Same question, same model (Claude Sonnet 4), 75 iterations.
The result:
gh CLI agent: 1,365 tokens
GitHub MCP agent: 44,026 tokens
A 32x gap.
Why such a gap?
It’s not a coincidence – it’s structural. An MCP server loads every registered tool's schema into context at the start of a session. The GitHub MCP has 43 tools. When the user asks "show me my PRs," Claude's context window also receives schemas for webhook management, gist creation, and repository settings – all unrelated to PRs.
CLI works differently. gh --help is called only when needed. The result drops in, gets used, and disappears. On top of that, Tier 1 CLIs like gh, git, curl, and aws are "familiar faces" that have appeared millions of times in the model's training data. The model already knows them. No schema needs to be re-explained.
The agent-first assumption has already shifted
This data has started turning the tide. Anthropic officially published a code execution pattern that drops the same workflow from 150,000 tokens to 2,000 – a 98.7% reduction. Cloudflare independently discovered the same pattern and called it Code Mode. MCP is not even old, but the assumption that "agent-first means MCP-first" is quietly breaking.
But CLI is not the answer either
Looking at the data, it is easy to conclude "fine, let us just go all-CLI." That’s a different trap.
CLI has clear limits.
First, auth separation is hard.
Imagine 50 people on a company Slack, each with a different channel visibility and different permissions. If the AI is typing commands on one person's computer, every action runs with that one person's permissions. There’s no clean way to separate who can do what.
Second, output is unstructured.
Run a command, and the result is a wall of text. The AI has to re-parse it every time: "ah, this number is the price, that is the date." Without a fixed schema, there is more room for mistakes.
Third, many services do not have a CLI.
Enterprise HR platforms like Workday, Greenhouse, and Lever never shipped a CLI, and probably never will.
The answer is not either-or
So the answer isn't MCP vs. CLI. The two tools were designed for different jobs. Once you can say in one sentence what that "different job" is, the question "where should I expose this functionality?" becomes easy to answer.
The missing axis: "where does the output go?"
These tools have been compared along two axes: who calls it (a human or a program), and how it connects (which protocol). But those two are not enough to explain why the four tools are genuinely separate things.
There is a missing axis: where does the output go?
Tool | Caller | Destination of the output |
|---|---|---|
API | Programs, other services | Wherever the caller wants it |
MCP | MCP-aware agent | The AI's context window (material for the current conversation) |
CLI | Shell (human or agent, typing commands) | Files on the machine, the next task |
Skill | Claude (per host) | The agent's next action (a procedure) |
Different destinations mean different fitting tasks. Same destination means effectively the same tool.
In short:
MCP sends output to context. CLI sends it to disk (the computer's workbench). Skill sends it to the AI's next action. API sends it to wherever the answer is needed.
Complements, not substitutes
These four tools are not substitutes. They are complements with different destinations.
Why we need both hands and feet
Why do humans have separate hands and feet? Both move and manipulate – yet they’re clearly different.
The answer is precision and distance. Hands handle small things up close with precision: lifting a cup, writing, typing. Limited reach, but high control. Feet cover distance and carry weight, but could never thread a needle.
The agent's tools work the same way.
MCP is the hand. Small data inside the context window, handled precisely through a schema, across multiple back-and-forth steps. Push 50KB of transcript through it, and the AI's hand fills up – the context breaks.
CLI is the foot. Moves large files inside the computer, pipes the output of one task into the next, and fits neatly into automated systems. Less precise than the hand because the output is unstructured text.
Skill is muscle memory. Memorizes sequences like "search notes, save transcript, summarize, post to Slack." Never touched the data itself.
API is the skeleton. Hands, feet, and muscles all attach to it. Move the skeleton, and everything else moves with it.
Why you need both
This is more than a metaphor. An agent operates with both a context window (short-term memory) and a filesystem (external memory). Short-term memory has a fixed capacity – try to carry too much at once, and it overflows. Heavy items go into external memory (the filesystem, the computer itself) and are pulled in only when needed. That’s the division of labor between hands and feet.
When to use which tool
Four questions you can answer in under a minute to decide "where do I expose this endpoint?"
Q1. Is the output large?
KB scale or larger, or files involved? CLI. Downloading 30 meetings at once, saving a transcript as markdown, piping a large NDJSON file – all CLI territory. Load this into a context window and reasoning slows down while token bills skyrocket.
Q2. Is this a one-shot task that does not need agent reasoning?
Then call the API directly from code. "Register this webhook," "deactivate this user" – that kind of work.
Q3. Do you need multi-turn reasoning over small metadata?
Light data, several steps: MCP. "Among my share links, find the ones that have not expired and extend each by 30 days" – that kind of work.
Q4. Does this sequence repeat?
Same steps every week, every PR? Skill. Write it once into a SKILL.md, and from then on it runs with a single prompt.
When one endpoint belongs to more than one tool
Sometimes a single endpoint needs to live in more than one tool. "Note metadata" can sit in MCP, while "the full transcript of the same note" lives in CLI for disk storage. The goal isn’t "expose to all four” – it’s to expose each endpoint to the one or two tools that match its destination.
Ask the destination first
In 2026, a SaaS product can support all four tools at once. The reason that feels heavy is that we tend to see them as variants of the same thing. They're not variants — they're four complements with different destinations.
A good design for AI Transformation doesn’t start with "which tool is better?"
It starts with: where does the output of this task need to go?
Get that one question right, and supporting all four tools becomes much more manageable.
Before asking "where do I expose this endpoint?", ask where the output needs to go.
FAQ
Q. What is MCP, in one line?
MCP stands for Model Context Protocol — a standard that lets an AI agent communicate with external tools and data in a consistent format. Released by Anthropic in November 2024. Runs on JSON-RPC. Major SaaS platforms (GitHub, Notion, Figma, Slack) provide official MCP servers.
Q. How is MCP different from an API?
Destination. An API sends the result wherever the caller wants it. MCP sends the result into the AI agent's context window. MCP is the right fit for multi-turn reasoning over small metadata; APIs are better suited for large-data work or direct calls from another service.
Q. Why does CLI use fewer tokens than MCP?
Two reasons. First, MCP injects all tool schemas into context at session start, while CLI only calls --help when needed. Second, major CLIs like gh, git, and aws appear millions of times in the model's training data, so no new schema needs to be introduced. In Scalekit's benchmark, CLI used 4x to 32x fewer tokens than MCP for the same task.
Q. Is Skill a replacement for MCP?
No. Skill doesn't access data directly — it defines a procedure in a SKILL.md file, like "search notes, summarize, send to Slack." MCP provides tool access. Skill is the guide for using those tools effectively. The two work best together.
Q. Should my service ship an MCP server too?
It depends on your data. If most of the work involves small metadata and multi-turn reasoning adds value, MCP is a good fit. If outputs are KB-scale or larger, or you're dealing with files, look at CLI first. The most realistic answer is: support the tools that match your use cases, but don't expose every endpoint to all four. Expose each endpoint to the tool that matches its destination.
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