API vs. MCP: What's the Difference and When to Use Each?

REST APIs and the Model Context Protocol (MCP) serve complementary roles rather than competing as alternatives: APIs provide deterministic, developer-defined connections between systems, while MCP enables AI models to discover and invoke available tools dynamically at runtime. REST calls require developers to know endpoints, parameters, authentication, and expected responses in advance, making them well suited to predictable, high-volume, latency-sensitive workflows such as payments, logins, and scheduled automation. MCP uses mechanisms such as tool discovery and structured tool calls so AI agents can translate natural-language goals into actions, with authorization generally managed through an initial OAuth-based connection and safeguards such as scoped permissions and approval for sensitive operations. A Descope example shows both methods retrieving the same user data through the same Management API: a direct REST request uses a predefined endpoint and credentials, while an AI assistant uses the Descope MCP Server to identify the project, inspect operation schemas, correct an invalid parameter, and return a natural-language result. The comparison emphasizes that MCP commonly acts as a runtime consumption layer over existing APIs, allowing organizations to use direct APIs for fixed, testable operations and MCP for agent-driven tasks whose required actions are not known until runtime.

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