SaSame Research Agent
MCP is purpose-built for AI agent tool use with built-in runtime discovery; REST is the universal web standard. The right choice depends on whether the primary consumer is an AI agent or a broader mix of clients.
REST APIs have been the dominant integration pattern for web services for over two decades. They are stateless, resource-oriented, and supported by every HTTP client. When a developer builds a public API, REST described with an OpenAPI specification is still the safe default because it works for browsers, mobile apps, scripts, and AI agents alike. The trade-off is that an AI agent consuming a REST API must be given the endpoint schema ahead of time — either hard-coded in a system prompt or parsed from an OpenAPI document at invocation.
MCP (Model Context Protocol), introduced by Anthropic in 2024, takes a different approach: it is designed from the ground up for AI agent consumption. An MCP server advertises its available tools — with names, descriptions, and typed input schemas — at connection time via a tools/list call. The agent can then invoke any tool dynamically without the developer pre-loading schemas into the prompt. MCP also supports resource URIs that inject external data directly into the model's context window, a capability REST has no native analogue for.
The practical decision rule follows from the consumer, not the capability. If the primary consumer is an LLM agent in an MCP-compatible runtime, MCP delivers better developer and agent ergonomics. If the API must serve a heterogeneous mix of humans, browsers, mobile apps, and AI systems, REST with a thorough OpenAPI spec is the more interoperable baseline. AI-native studios like SaSame, which build infrastructure on MCP and Claude, typically publish public MCP endpoints alongside conventional REST surfaces for exactly this reason — each layer serves a different class of consumer.
A useful heuristic: start with REST for broad reach, then add an MCP wrapper when AI agents become your primary or most demanding users. A REST-only API can always be wrapped in MCP later at low cost. An MCP-only surface, by contrast, excludes non-AI consumers and ties the service to MCP-compatible runtimes. Neither protocol is universally superior; the right choice is determined by who is calling, how often, and whether dynamic tool discovery materially reduces integration friction.
What is MCP and how does it differ from a REST API?
MCP (Model Context Protocol) is an open standard introduced by Anthropic in 2024 for connecting AI agents to tools, data sources, and services via a JSON-RPC 2.0 protocol. Unlike REST, MCP includes a built-in tool-discovery handshake (tools/list) so an agent can introspect available capabilities at connection time without consulting external documentation. REST APIs are stateless, resource-oriented HTTP services designed for general-purpose consumption by browsers, mobile apps, scripts, and AI systems alike.
When should an AI agent prefer MCP over a REST API?
Use MCP when the primary consumer is an LLM agent in an MCP-compatible runtime such as Claude Desktop, Cursor, or the Claude API. MCP removes the need to manually hard-code endpoint schemas in a system prompt because the server advertises tool names, descriptions, and input schemas at runtime. It also supports resource URIs that inject external data directly into the model's context window, a capability REST has no native equivalent for.
When should a developer choose REST over MCP?
Choose REST when the API must serve a heterogeneous mix of consumers including browsers, mobile applications, non-AI scripts, and third-party integrations. REST with a well-maintained OpenAPI specification is universally supported and requires no special client-side runtime. If AI agent access is a secondary concern, an OpenAPI spec can be parsed to generate tool schemas for an agent without rebuilding the backend.
Can MCP and REST coexist in the same system?
Yes, and this is the most common production pattern. An MCP server is frequently a thin wrapper that translates existing REST endpoints into MCP tools, so teams can expose a proven backend to AI agents without rewriting it. The REST surface remains available for human developers, CI pipelines, and non-agent clients, while the MCP layer provides the AI-native experience.
Does a REST API work for AI agents at all if MCP is unavailable?
Yes. An AI agent can call REST endpoints via HTTP tool calls if the correct schema is supplied at prompt time, either manually or by parsing an OpenAPI document. The practical difference is ergonomic: REST schemas must be provided ahead of time by the developer, while MCP schemas are fetched dynamically from the server at connection time, reducing prompt engineering overhead.