MCP vs API: Which One Should Your Agency Care About?

Short answer

An API lets developers connect software through fixed endpoints; MCP is a standard layer on top of APIs that lets AI tools like Claude or ChatGPT use those connections in plain language. Agencies should care about MCP first, for day-to-day questions like client briefings, and keep APIs for scripted, deterministic automation.

When does MCP beat an API for an agency?

MCP wins when a person is asking open-ended questions in plain language and the tool they use already offers an MCP connection. An API wins when a developer needs the same job to run identically every time. Mitiga's rule of thumb is similar: APIs for deterministic, scripted automation you control line by line, MCP for AI agents and analysts working in natural language.

Here's what that looks like. It's Monday morning and you run a six-person studio. You ask Claude, "What's waiting on each client, and what's stuck?" Claude reads your client portal through MCP and gives you a briefing from real requests, files and approvals. Nobody wrote a script, and you didn't open eleven tabs.

Two conditions have to be true. Your AI app must support MCP, and the tool holding your client data must offer an MCP connection. ChatGPT connects to Dora this way, as do Claude, Claude Code and Codex. Most of the research on MCP comes from vendor and community blogs, so check current support in your own plan before you promise anything to your team.

Don't start by asking your developer to build a custom API integration so your AI can read client data. Check first whether the tool already offers an MCP connection. For an agency lead's daily questions that's faster and needs no code. Save the API for jobs where exact repeatability matters, like a nightly export.

Four questions settle most decisions:

Who is doing the work? A developer writing code points to an API. A person asking in plain language points to MCP.
Must it behave identically every time? If yes, use an API. If it's an open-ended request, use MCP.
Is there already an MCP connection? Check that your tool offers one and your AI app supports it.
What may the AI read, and what may it change? Decide this up front, and keep approvals with your team.

What is the real difference between MCP and an API?

An API connects software to software through predefined endpoints. MCP lets an AI model discover and use tools without a developer hardcoding each connection. That's the core contrast in TrueFoundry's comparison.

Put another way: MCP is a standardized layer that wraps existing APIs, so the AI doesn't need custom code for every connection, as Codecademy explains. MeasureOne describes the contrast as static and endpoint-driven (APIs) versus dynamic and intent-driven (MCP servers).

	API	MCP
Built for	Developers wiring up software	AI models like Claude or ChatGPT
How you use it	Code calls fixed endpoints	You ask in plain language; the AI picks the tools
Predictability	Same call, same behavior	Flexible, shaped by what you ask
Who sets it up	A developer, per integration	You connect your AI app to a tool that offers MCP
Agency example	Nightly script that copies approved files to a backup folder	"Which clients have approvals waiting more than a week?"

A few quick answers to the questions people ask next:

Does MCP replace APIs? No. It builds on top of them, and APIs still do the actual work underneath.
Is MCP just a fancy API? It's built on APIs, but its audience is different. One Hacker News commenter framed MCP as a UI optimized for LLMs, which is a handy way to think about it, though it's a discussion comment, not a spec.
Is MCP like an API gateway? Not really. A gateway sits in front of backend services to control traffic. MCP exposes a curated set of tools to an AI.

How does MCP work with a client portal like Dora?

With Dora, you connect your AI app once, then ask it about client work in plain language. Dora is an AI account manager and client portal for agencies, studios and service teams. Claude, ChatGPT, Claude Code and Codex all connect to it over MCP, on every plan, for your team and for clients.

Here's the flow:

Connect your AI app. For example, connect Claude to your portal by following the steps on the integration page.
Ask for a briefing. "Summarize what's open for each client and flag anything missing a brief."
Have it create or update a request. Dictate a client's ask and let the AI turn it into an open request in the portal.
Review before anything changes. Decisions about the work stay with your team.

Dora's own AI account manager handles the client-facing side: asking for what's missing, writing briefs and chasing sign-off. Connecting your own AI adds a way for you to query and feed that same data. For the longer walkthrough, see our guide to MCP for agencies.

Some honest tradeoffs:

There's no one-click import yet. A connected AI like Claude can create open requests through Dora's MCP, which is the practical workaround.
If you want invoicing, contracts or internal project planning in the same tool, Dora isn't that.
If you need exact, repeatable automation, an API is still the right call.

MCP adds access, so decide what your AI can read and what it can change. Auth0 argues that raw APIs overwhelm AI agents with choices and that MCP helps, but remember it's a vendor blog with a commercial angle. On Dora's side, how client data is kept safe is spelled out, and client content isn't used to train models.

You can test this without a big commitment. Time how long it takes today to assemble a briefing for three clients from your inbox, files and request threads. Then connect your AI and ask for the same three briefings. After one week, compare the time spent and the details that were missing.

Connect Claude or ChatGPT to your portal and ask for your first client briefing: try Dora free, since your first client is free, forever.

Does MCP replace APIs?

No. MCP sits on top of APIs, and the APIs still do the actual work underneath. APIs remain the right choice for scripted automation where a developer wants the same result every time.

Is MCP just a fancy API?

Not quite. It's built on APIs, but it's aimed at AI models that discover tools on their own, not at developers hardcoding endpoints. The simplest way to remember it: an API is for code, MCP is for an AI doing a task for a person.

Does ChatGPT use MCP?

Yes, ChatGPT can connect to tools over MCP, including Dora. Claude, Claude Code and Codex connect to Dora the same way. Support can vary by plan and change over time, so check your current ChatGPT plan before relying on it.

Do I need a developer to use MCP?

Not to use a tool that already offers an MCP connection. You connect your AI app, then ask in plain language. You'd only need a developer to build an MCP server for a tool that doesn't have one, or when you want an exact, scripted job, which is where an API fits better.