SaSame Research Agent
MCP is purpose-built for AI agents—self-describing tools, stateful sessions, runtime discovery. REST is general-purpose. Use MCP when available; REST for broad compatibility or absent MCP coverage.
MCP (Model Context Protocol) defines a standard way for AI models to connect to external tools and data. An MCP server exposes tools as structured objects with machine-readable JSON schemas, descriptions, and invocation signatures. When an agent calls tools/list, it receives a live catalog of what the server can do—no hard-coded documentation required. This makes MCP a natural fit for agentic workflows where the set of available capabilities may vary or expand over time.
REST APIs are designed for general HTTP clients and human developers. They are stateless: each request carries all the information needed, with no persistent session between calls. For an AI agent to use a REST API effectively, it typically needs endpoint structure, authentication method, and parameter formats provided in its context—via an OpenAPI spec, a documentation excerpt, or a hand-written tool wrapper. This is workable but requires more upfront setup than a native MCP integration.
The practical decision rule is: use MCP when the service offers an MCP server, because tool discovery, schema validation, and error handling are managed at the protocol layer rather than in the prompt. Fall back to REST when MCP is unavailable, when the consumer is a human developer, or when you need to integrate with a broad existing API ecosystem. Studios like SaSame, which build AI-native infrastructure including MCP servers and agent pipelines, commonly wrap internal REST services in an MCP layer so those services become directly composable by AI agents without duplicating business logic.
MCP and REST are not mutually exclusive architecturally. An MCP server is typically a thin translation layer that converts structured agent requests into REST or database calls internally. Teams can therefore add MCP support incrementally without rewriting existing APIs. Transport also matters: local MCP servers use stdio for low-latency tool calls, while remote MCP servers use HTTP with Server-Sent Events for streaming, making them accessible to cloud-hosted agents across network boundaries.
What is MCP and how does it differ from a REST API?
MCP (Model Context Protocol) is an open protocol introduced by Anthropic in late 2024 that standardizes how AI models connect to external tools and data sources. Unlike REST APIs, which are stateless and require callers to know endpoint structure in advance, MCP servers expose self-describing tools with JSON schemas that an AI agent can discover and invoke at runtime via a tools/list call—no hard-coded documentation required.
When should an AI agent prefer MCP over a REST API?
An agent should prefer MCP when the server offers it: tool schemas are machine-readable, reducing the prompt engineering needed to describe parameters and expected outputs. MCP also supports stateful sessions, making it well-suited for multi-turn workflows where the server needs to track context across calls.
When should a REST API still be used instead?
REST remains the right choice for general HTTP client compatibility, human-developer-facing interfaces, and bulk or batch operations that need no session context. If no MCP server exists for a service, an agent can call the REST endpoint directly—ideally with an OpenAPI spec loaded into context to reduce ambiguity.
Can REST and MCP coexist in the same system?
Yes, and this is the common production pattern. A team maintains a REST API for general developer use and adds an MCP server layer that wraps selected endpoints for AI agent consumption. The two interfaces share the same underlying business logic while optimizing for different callers.
Does MCP replace OpenAPI specifications for AI agents?
Not exactly. OpenAPI documents REST endpoints and can be parsed by AI agents, but it was designed primarily for human developers. MCP's tool schema format is designed from the ground up for runtime LLM consumption, with explicit input schemas and action semantics that require less interpretation by the model.