Can REST APIs and MCP work together?

Yes, and they commonly do. An MCP server often wraps an existing REST API — the AI agent calls the MCP server, which internally makes REST calls to the underlying service. REST handles the data layer; MCP handles the AI interaction layer.

When should I use MCP instead of a direct REST API call?

Use MCP when you are building AI agents that need to select and invoke tools dynamically based on context — where the agent decides at runtime which tool to call and with what parameters. Use a direct REST API call when you have a fixed, code-defined integration between two software systems with no AI decision-making involved.

B2B Portals

REST API vs MCP

A REST API is a standard interface for software systems to exchange data. MCP (Model Context Protocol) is a protocol for AI agents to connect to tools and data sources at runtime. They solve different problems — and in most AI-integrated systems, they work alongside each other.

By Maksym Miedvied

REST APIs have been the standard for software integration since the mid-2000s. A REST API exposes endpoints — URLs that respond to HTTP methods (GET, POST, PUT, DELETE) — and returns structured data, typically JSON. When your accounting software connects to your CRM, when your e-commerce platform checks shipping rates, when a mobile app authenticates a user — those are REST API calls. The integration logic is fixed in code. System A knows exactly which endpoint to call, with exactly which parameters, and the response format is defined in advance.

MCP (Model Context Protocol) is a different layer entirely. It was designed for the problem of connecting AI language models to external capabilities at runtime. An AI agent working through a task does not know in advance which tools it will need — it reasons about the task and decides which tool to call based on context. MCP provides a standardised way for that agent to discover available tools, understand what each tool does and what parameters it accepts, and invoke the right tool at the right moment. The agent is not following a predetermined script; it is making decisions.

In practice, MCP servers often wrap REST APIs underneath. A company builds an MCP server that exposes their internal REST API in a way that an AI agent can understand and use. The agent calls the MCP server; the MCP server calls the REST endpoint; the result comes back to the agent. REST handles the data layer between systems; MCP handles the AI interaction layer between the model and its tools.

For a B2B portal, this distinction matters when considering AI-assisted workflows. A standard portal integration — distributor logs in, places an order, the system processes it — is built entirely on REST. Adding an AI agent to that portal — one that can look up stock levels, suggest alternatives for out-of-stock items, draft purchase orders based on a conversation — introduces MCP as the protocol connecting the agent to those data sources. You do not choose one over the other. You use REST for the application integration and MCP for the AI layer that sits on top.

Key Points

REST API: application-to-application data exchange over HTTP, fixed integration logic
MCP: AI agent-to-tool communication, where the agent selects tools dynamically at runtime
MCP servers commonly wrap existing REST APIs underneath
REST suits deterministic, code-defined integrations between known systems
MCP suits AI agents that need to reason about which action to take and when
Both can coexist in the same system — they operate at different layers