MCP vs API vs plugin: what is the difference?

Authorize a compatible MCP client to read project context and record supported memory operations. Access respects workspace permissions; an AI answer still needs review.

API, plugin and MCP are often treated as interchangeable words for “connection”. They are not. An API is an interface exposed by software. A plugin is an installable extension for a particular host product. MCP is a protocol designed so an AI host can discover and use capabilities provided by servers in a consistent way.

They can also coexist. An MCP server may call an API behind the scenes, while a host application may package its MCP support inside a connector or extension. The useful question is not which term wins, but which layer solves your problem.

API: a contract between software systems

An application programming interface defines how software can request data or operations from another system. It usually documents endpoints or functions, parameters, authentication, responses and errors.

An API does not decide how an AI assistant should discover its capabilities or when it should call them. A developer normally writes that integration logic. APIs are ideal for controlled application-to-application workflows, backend automation and custom interfaces.

Plugin: an extension tied to a host

“Plugin” is a product term, not one universal interoperability standard. A WordPress plugin, browser extension and design-app plugin follow different packaging, permissions and distribution rules. They extend a specific host and normally depend on its SDK or marketplace.

Plugins are useful when the goal is to add interface, commands or behaviour directly inside that product. The trade-off is coupling: an extension made for one host is not automatically usable by another.

Calling every AI integration a plugin hides important questions. Where does it run? Which host controls it? What permissions does it receive? Can another client use the same server?

MCP: a protocol for AI hosts, clients and servers

The official MCP architecture separates three roles. The host is the AI application that coordinates permissions and user decisions. It creates clients, and each client maintains a connection with one MCP server. Servers expose capabilities such as tools, resources and prompts.

This common structure lets a compatible host discover what a server offers instead of requiring a bespoke prompt convention for every service. It does not mean that every client supports every capability, or that connecting a server grants unlimited action.

Read what an MCP server is for the full architecture.

The differences at a glance

Question	API	Plugin	MCP
What is it?	Software interface	Host-specific extension	Protocol for AI context and capabilities
Who integrates it?	Usually a developer	Plugin author and host	MCP server and compatible client
Discovery	Defined by API docs/code	Host marketplace or manifest	Capability negotiation and MCP primitives
Portability	Reusable, but integration code is custom	Usually tied to one host	Reusable across compatible clients, subject to support
Interface included	Not necessarily	Often yes	Usually presented by the AI host
Permission model	API-specific	Host-specific	Host consent plus server authorization where implemented

MCP does not replace APIs

The MCP server still needs a way to perform real work. It may query a database, call internal services or use third-party APIs. MCP standardises the interaction with the AI client; it does not replace every underlying integration.

MCP does not automatically mean safe

A protocol provides structure, not a blanket guarantee. Inspect the server URL, publisher, requested permissions and proposed action. Prefer least privilege, keep write approvals visible and revoke access you no longer need.

For remote HTTP servers, the current MCP authorization specification uses an OAuth-based framework when authorization is implemented. Credentials should travel in authorization headers, not in URL query strings. Users should still follow the exact in-app flow because transport and client support can change.

Which one should you choose?

Choose an API when you are building a custom application workflow and control the code. Choose a plugin when you need a host-specific interface or extension. Choose MCP when you want compatible AI clients to discover and use authorised context and actions through a shared protocol.

Often the architecture uses all three: an extension presents controls, an MCP server coordinates the AI interaction and APIs perform the underlying work.

Frequently asked questions

Is MCP just an API for AI?

That shortcut is incomplete. MCP includes a host-client-server architecture, capability negotiation and AI-oriented primitives; an API is a broader interface concept.

Does MCP give the AI access to everything?

No. Available capabilities and returned data depend on the server, authorization, account and workspace permissions.

Can one MCP server work in every AI client?

Only in clients that support the relevant MCP transport, authorization and capabilities.

What BeKnow keeps

Keep interventions, hypotheses, assets, observations and decisions in a searchable workspace. Record what is known and what still needs checking.

A change in performance after an intervention does not prove that the intervention caused it. Cross-platform attribution and a complete analytics dashboard are not available today.

Project memory and data imports do not require an AI model key. A compatible external AI client may have its own costs. BYOK applies only to available functions that actually call an external provider.

Next step

Start with one project, one documented change and the evidence needed to review it. Source connections.

Record your first intervention. How it works.