MCP 與 API：額外層級帶來的優勢（我的 10 個業務代理團隊零 MCP 呼叫）

MCP vs API is the wrong fight. MCP doesn’t replace an API. It sits on top of one. An API is the door into a piece of software. MCP (the Model Context Protocol) is a standard way to hand that door to an AI model, with a description of what’s behind it, so the model can find it and use it without you writing custom glue code for every app.

So the real question isn’t “which one is better.” It’s this: does your AI need that extra layer, or can it just call the API?

I can answer that with receipts. I run 10+ autonomous brand containers on Claude Code. This blog post was researched, written, illustrated and published by one of them while I slept. And here’s the part most MCP vs API guides would find awkward: across the 39 skill files that run this business, there are zero MCP tool calls. Every integration is a plain API call. That isn’t because MCP is bad. It’s because of the kind of work my agents do. This guide shows you how to tell which kind of work yours does.

MCP vs API: The Short Answer

Here’s the whole MCP vs API comparison in one table. If you only read one section, read this one.

Question	API	MCP
What is it?	A set of endpoints a program can call (usually HTTP + JSON)	An open protocol for exposing tools and data to AI apps in one standard shape
Who is it built for?	Developers writing code that knows exactly what to call	AI clients (Claude, ChatGPT, Cursor, IDEs) that decide at runtime what to call
How does the caller learn what’s available?	A human reads the docs	The client asks the server, which lists its tools with descriptions and input schemas
Does it replace the other?	不	No. Most MCP servers are a wrapper around an existing API
最佳匹配	Repeatable jobs where the steps are known in advance	Open-ended, interactive work across many tools and many AI apps
Main cost	You write and maintain the integration code	Tool descriptions take up model context, and there’s one more moving part to run and secure

If that table is all you needed, here’s the one-line rule I use: if you can write the steps down before the job starts, call the API. If the AI has to figure out which tools to use while it works, MCP starts to earn its keep.

The rest of this guide shows why that rule holds, where it breaks, and what each choice actually costs you in money, context and maintenance.

What an API Is, and What MCP Adds on Top

Most MCP vs API articles explain both in the abstract. Let’s use a real example from my stack instead: publishing this post to WordPress.

The API layer

WordPress has a REST API. To create a post, you send an HTTP POST to /wp-json/wp/v2/posts with a title, the content and a status, plus your credentials. That’s it. It works the same whether a human, a script or an AI sends it. The API doesn’t know or care who’s calling.

APIs are built for developers who already know what they want. You read the docs once, write the call, and from then on the same call runs the same way every time.

The tool layer (function calling)

Language models can’t make HTTP requests on their own. So AI platforms let you describe a “tool”: a name, a plain-English description and a list of inputs. The model reads the description, decides when to use it, and fills in the inputs. Your code then makes the real API call. This is called function calling or tool use, and it’s been around since well before MCP.

The MCP layer

Function calling has one annoying problem. Every AI app wants tools described in its own format, and every developer writes the same WordPress (or Slack, or GitHub) wrapper over and over. Anthropic released MCP in November 2024 to fix that. An MCP server wraps a service once, in one standard shape, and any MCP-capable app can connect to it.

Under the hood, an MCP server offers three kinds of things:

• 工具： actions the model can take (“create_post”, “search_issues”).
• Resources: data the app can read into context (a file, a record, a page).
• Prompts: reusable templates the server suggests for common jobs.

The client asks the server “what have you got?”, gets back a list with descriptions and input schemas, and shows that list to the model. That’s the big difference from a plain API: discovery happens at runtime, not in a developer’s head.

An API answers “how do I do this?” MCP answers “what can I do here?” If your agent already knows the answer to the second question, it only needs the first.

One more thing the top-ranking guides get wrong now: several still describe MCP as “stateful” and APIs as “stateless” as if that’s a law of nature. The MCP spec revision dated 2026-07-28 removed the initialize handshake and the session ID, and each request now carries its own protocol version and capabilities. So that particular argument has aged out. I covered what that change means for routing and gateways in my MCP閘道故障.

My Receipt: 39 Skill Files, 0 MCP Calls

Here’s what the MCP vs API split looks like in an autonomous business that actually runs. I counted every external service reference across the 39 skill files in this brand’s container tonight (2026-10-25). A “skill file” is the written instruction set one scheduled agent follows, like “write the blog post” or “check the inbox”.

服務	How the agents reach it	References in skill files
Airtable (source of truth)	REST API via curl	56
WordPress	REST API via curl	41
DataForSEO	REST API via curl	13
體式	REST API via curl	12
Metricool (social scheduling)	REST API via curl	12
ScrapeCreators	REST API via curl	11
火行者	REST API via curl	6
Instantly (outreach)	REST API via curl	6
Any MCP tool	不適用	0

It gets better. My own brand config file claims two MCP servers are connected for task management and image generation. They aren’t. The only MCP server actually configured in this container is a leftover from a sister brand, and it rejected its auth header again at the start of tonight’s run, the same 401 it’s thrown since at least August. Nothing broke. Not one job noticed, because no job depends on it.

That’s not a flex, it’s a diagnosis. My agents do the same jobs on a schedule: pick a keyword, research the top results, write, generate images, publish, log the result. Every step is known before the job starts. The skill file spells out the exact endpoint, the exact field names, even the gotchas. (Airtable reads ignore field-name casing, but writes don’t. That one cost me weeks of silent failures before it went into the instructions.)

When the steps are written down, the agent doesn’t need to discover anything. It needs to execute. And for execution, a direct API call has three things going for it:

• It’s debuggable. When something fails at 3am, the log shows the exact request and the exact error. I can copy the call, run it myself, and see the same thing.
• It’s cheap on context. The agent only loads the instructions for tonight’s job, not a menu of every tool in the business.
• It has fewer moving parts. No server process to keep alive, no extra auth layer, no protocol version to track.

If you want the bigger picture of how these scheduled agents are set up, I wrote up 真正支撐我旗下10個品牌業務生產的是什麼？, and the instruction file that holds it all together.

When MCP vs API Tips Toward MCP

None of this means MCP is hype, or that the MCP vs API debate always ends with the API. It means MCP solves a different problem than mine. Here are the five situations where I’d reach for it, and where I do reach for it when I’m working interactively rather than on a schedule.

1. Many AI apps need the same integration

If your team uses Claude on the desktop, Cursor in the editor and a custom agent in production, and all three need your CRM, writing three wrappers is silly. One MCP server, three clients. This is the problem MCP was designed for, and it’s where the standard pays off fastest.

2. The work is open-ended

“Find every customer who complained about shipping last month and draft a reply to each” is not a job you can script in advance. The AI needs to look around, decide which tools matter, and chain them. Runtime discovery is exactly what MCP gives you.

3. The person using it doesn’t write code

A sales manager can connect a ready-made MCP server to Claude in a few clicks. They can’t write curl commands. For non-technical teams, MCP is often the only practical way to put a tool in front of an AI at all.

4. The vendor maintains an official server

When the company behind a tool ships and maintains its own MCP server, you’re borrowing their upkeep. Endpoint changes, auth changes and new features become their problem. That’s a real saving, as long as the server is official and actively maintained. I keep a running list of the MCP servers I’d actually trust.

5. You don’t know the tool list at build time

If you’re building a product where your users bring their own tools, you can’t hard-code every API. MCP lets your app accept integrations you’ve never seen. If you’re curious how that extends to interactive screens, see MCP Apps explained.

Notice the pattern in every MCP vs API decision. MCP wins when there’s uncertainty: about which tools, which clients, or which user. An API wins when there’s certainty: the same job, the same steps, every night.

The Hidden Costs of MCP vs API Nobody Prices In

Most MCP vs API comparisons stop at “MCP is more flexible.” Flexibility has a bill. Here’s what shows up on it.

Context: the menu costs tokens on every turn

Every tool an MCP server exposes comes with a name, a description and an input schema. All of that is text the model has to read. Connect four servers with dozens of tools each and a meaningful slice of the model’s working memory is spent on a menu, every turn, for tools the current job might never touch. That’s slower, more expensive, and can make the model worse at picking the right tool.

The fix is curation. Connect only the servers a job needs. Prefer servers with a few task-shaped tools over ones that mirror every endpoint. Some clients now help here: Claude Code, for example, can hold back tool schemas and load them through a search step when the list gets long. I see it in my own interactive sessions. It helps, but it’s a patch for a menu that’s too big, not a reason to make the menu bigger.

Credentials: one more thing holding your keys

An MCP server needs access to the underlying service, which means it holds your credentials or a token. Every server you add is another place a key lives and another process that can be misconfigured. With direct API calls in my containers, credentials come from environment variables and nowhere else. I can list them, rotate them and audit them in one place.

Debugging: the error is one layer further away

When a direct call fails, I get the HTTP status and the response body. When an MCP tool fails, the error passes through the server, then the protocol, then the client, before the model sees it, and sometimes it arrives as a polite paragraph instead of a status code. For an unattended agent, that distance matters. My whole operation depends on reading a failure the next morning and knowing exactly what happened.

Change: the protocol is still moving

The MCP spec has had several revisions since late 2024, including the stateless change in July 2026. That’s healthy for a young standard, but it means servers and clients have to keep up. HTTP APIs have been stable for decades. If you’re building something that must run for years with minimal care, that’s worth weighing.

成本	Direct API call	MCP伺服器
Upfront build	Higher: you write the call and the instructions	Lower if a good server exists; higher if you build one
Context per turn	Only what tonight’s job needs	Every connected tool’s description
Where keys live	Your env vars	Your env vars plus each server’s config
偵錯	Raw status code and body	Filtered through server, protocol and client
Ongoing upkeep	You track API changes	Server maintainer tracks API changes; you track the server and the spec

A 4-Question Filter: MCP, API, or Both?

Run any integration through these four questions before you settle the MCP vs API call. It takes two minutes and saves a lot of second-guessing.

1. Can you write the steps down before the job starts? If yes, start with a direct API call. If the AI needs to explore and choose, lean MCP.
2. How many AI apps need this integration? One agent: an API call (or a simple tool definition) is enough. Several apps or a whole team: an MCP server stops you writing the same wrapper three times.
3. Who’s going to use it? A developer or a scheduled agent: API. A non-technical person in a chat app: MCP.
4. Does an official, maintained server exist? If yes and you passed questions 2 or 3 toward MCP, use it. If the only option is an abandoned community server, write the API call yourself. You’ll own less risk.

Most real businesses end up with 兩個都. My own split looks like this: scheduled agents that run the business call APIs directly, every time. When I’m working interactively, exploring data, poking at a new tool or helping a client, MCP servers are genuinely handy, because that work is open-ended. Same person, same tools, different job shape.

If you want a starting list of the tools that sit in that interactive lane, my guide to MCP 工具以及我每天運行的工具 值得接下來閱讀。.

And if you’d rather not sort through this alone: deciding which jobs should run headless on plain API calls, and which ones need a person and an MCP-connected assistant, is exactly the first session of the work I do with clients. You can 預約自動化策略會議 and we’ll map it for your stack.

MCP vs API FAQ

Is MCP replacing APIs, or is MCP vs API a false choice?

No. Almost every MCP server calls an API underneath. MCP is a standard way to present that API to an AI model. If the API disappeared, the MCP server would stop working too.

Is an MCP server just an API?

Technically it’s a server that speaks a specific protocol (JSON-RPC messages, over a local process or HTTP). Practically, think of it as an adapter: it takes an existing API and describes it in a way any MCP-capable AI app can understand.

Is MCP more secure than giving an agent direct API access?

Not automatically. Both come down to what the credential can do. A narrowly scoped API key used directly is safer than a broad one sitting inside an MCP server, and the reverse is also true. Scope the key, limit the actions, and log every call, whichever route you take.

Do I need MCP to build an AI agent?

No. My agents run 10+ businesses without a single MCP call. You need the agent to reach your tools somehow: direct API calls, function calling, a command-line tool or MCP. Pick by job shape, not by trend. If you’re just starting out, my walkthrough on what an AI coding agent is and how to test one covers the basics.

Is MCP slower than calling the API directly?

There’s a small extra hop through the server, but the bigger cost is usually context: the model reading tool descriptions on every turn. For real-time or high-volume jobs where no AI decision is needed, skip the model entirely and call the API from plain code.

Can I use MCP with no-code tools like n8n or Zapier?

Yes. Several automation platforms can now act as an MCP client or expose your workflows as MCP tools. If you’re weighing a visual builder against code-first agents, my field guide to n8n covers where each one fits.

Final Thoughts: Pick the Layer by the Job, Not the Hype

MCP vs API isn’t a rivalry. It’s a question about how much your AI needs to figure out on its own. If the job is known, written down and repeated, a direct API call is simpler, cheaper on context and easier to fix at 3am. That’s why the agents running this business, including the one that published this post, use zero MCP calls. If the job is open-ended, shared across several AI apps or used by people who don’t write code, MCP is the right adapter and it’ll save you a pile of glue code.

So don’t start by asking which technology is better. Start by writing down one job you want an agent to do. If you can write every step, wire it to the API and let it run. If you can’t, give your assistant an MCP server and let it explore. Then watch the logs for two weeks and let the results decide what you build next.

If you’d like help picking that first job and wiring it the boring, reliable way, 預約自動化策略會議.