MCP Connector vs API vs Plugin: What Is the Difference?

These three are not competing products so much as three layers of the same stack. The distinction that makes them click into place is standardization: is this integration reusable across many AI assistants, or built for exactly one? Using a brokerage connection as the example:

• API. The broker publishes an interface that software can call to read holdings or place an order. It is provider-specific and code-level. Everything else is usually built on top of it.
• Plugin. One AI product builds a bespoke integration to that broker, wrapping the API for its own host. It works inside that one assistant and nowhere else.
• MCP connector. Someone builds one connector to the broker on an open standard, and any MCP-capable assistant can plug into it through the same interface, no per-host rebuild required.

So the API is the plumbing, a plugin is a one-host adapter on that plumbing, and an MCP connector is a standard adapter that many hosts can share. They stack more than they compete.

An API, or application programming interface, is the raw, foundational layer. It is the set of endpoints a service exposes so other software can talk to it directly.

In the brokerage example, the broker’s API is what actually returns your positions or accepts an order. It is powerful and precise, but it speaks to code, not to people, and it is unique to that provider. That is exactly why the other two layers exist: to make that raw capability usable without every user or every AI product reinventing the wiring.

A plugin is the one-host layer. It takes an underlying API and packages it specifically for a single AI product, so that product can reach the service without the user touching code.

A brokerage plugin built for one assistant lets you enable it in that app and start asking questions. The convenience is real, and the limit is just as real: the integration lives inside that one host. If you switch assistants, or want the same broker reachable from a different product, someone has to build the plugin again for that product. Multiply that across every service and every AI host and you get a many-to-many problem, which is the gap the standard fills.

An MCP connector is the standard layer. It uses the Model Context Protocol, an open specification for how AI assistants connect to outside tools and data, so a single connector can serve any assistant that speaks MCP.

For the brokerage example, an MCP connector to the broker means one integration that any MCP-capable assistant can use, instead of a separate plugin per product. It usually still sits on top of the broker’s API; the value it adds is the shared shape. For more on the concept, see what is an MCP connector and the roundup of MCP connectors for investing.

The reason MCP is spreading is simple economics of integration. Without a shared standard, connecting one service to several AI products means building a separate plugin for each, and connecting several services to several products multiplies the work. That is the classic many-to-many problem.

• Build once, reuse everywhere. A service builds a single MCP connector, and every MCP-capable assistant can plug into it, rather than each product needing its own custom integration.
• Portability for users. If you switch assistants, an MCP connection can carry over instead of being stranded inside one host the way a plugin is.
• It is open, not owned. Because the protocol is a shared standard rather than one company’s add-on format, more hosts and tools can adopt it without asking permission.
• Good fit for connecting real systems. For something like a brokerage, where you want one reliable connection an assistant can read and reason over, a reusable standard is a better foundation than a stack of one-off plugins.

None of that makes MCP a silver bullet: the standard is young, tooling is still maturing, and it only helps with assistants that support it. But the direction of travel, one integration that many assistants share, is why it keeps coming up.

The right layer depends on who you are and what you are trying to connect. Using the brokerage-to-assistant example again:

• You are a developer building your own tool. You may work with the broker’s API directly for full, low-level control, accepting that it is provider-specific and code-level.
• You live inside one AI product. If that product offers a plugin for your broker, it is the simplest path within that app, as long as you are fine being tied to that one host.
• You want the connection to work across assistants. An MCP connector is the portable option, so the same brokerage connection can serve more than one MCP-capable assistant.
• You just want to use it, not build it. Look for a product that packages the connector for you, handles setup, and connects your account. Walnut is one example of that pattern for a brokerage.

If you are weighing whether to roll your own, the question of whether to build your own MCP server walks through the tradeoffs.

To keep the example concrete, and to be upfront since this is our site: Walnut is one product that uses the connector-style pattern rather than asking you to call a raw API yourself. It is an AI investing assistant that lets you chat about the portfolio on a broker you already own, connecting through SnapTrade, which is read-only by default. The point of naming it here is not that it is the only or best way to connect a broker; it is a familiar illustration of what “a connected assistant” means in practice.

The connection reads your holdings so the chat is grounded in what you actually own, and because it is read-only by default the assistant can research and frame options without acting on its own. Any trade requires your explicit approval, and Walnut is informational and is not an investment adviser. The takeaway is the layering, not the pitch: under most “connect your broker to an AI” experiences sits an API, and the part that makes it usable across assistants is the standard connector on top.

An API, a plugin, and an MCP connector are three layers of the same job, not three rivals. The API is the raw, provider-specific interface software calls. A plugin wraps that for one AI host and stays locked to it. An MCP connector wraps it on an open standard, so one integration works across any MCP-capable assistant, which is why the pattern is gaining traction for connecting real systems like a brokerage. Pick the API if you are building, a plugin if you only care about one host, and a connector if you want portability, or use a product that packages the connector for you. Walnut is one such example; it is not an investment adviser.