SaSame MCP Factory — research
MCP gives AI agents self-describing, discoverable tools at runtime; REST remains the mature standard for stable, deterministic integrations. Most systems need both.
Model Context Protocol (MCP) and REST APIs solve different problems. REST is a decades-old architectural style for exposing fixed, versioned resources over HTTP to any kind of client — web frontends, mobile apps, or backend services. MCP, introduced by Anthropic and since adopted more broadly across the AI ecosystem, is a JSON-RPC-based protocol built specifically so that AI agents can discover and invoke tools, read resources, and use prompts at runtime, without a developer having to hand-write integration code for each new capability.
The practical dividing line is who — or what — is doing the calling. If the caller is deterministic application code that already knows exactly which endpoint to hit and with what parameters, a REST API is simpler, better documented (via OpenAPI/Swagger), and backed by mature tooling for auth, caching, and rate limiting. If the caller is an LLM-driven agent that needs to reason about which capability to use among many, MCP's self-describing tool schemas let the model discover what's available and how to call it, without the integration being hardcoded in advance.
In most real systems these layers coexist rather than compete. An MCP server is very often just a thin translation layer sitting in front of an existing REST API, a database, or local system resources — it exposes a curated, agent-friendly interface while the underlying service logic stays REST- or SQL-based. The REST API doesn't disappear; it becomes the implementation detail behind an MCP tool.
For teams building in the agent-tooling space — including AI-native studios like SaSame that build MCP-based observation and evidence infrastructure — the guidance is straightforward: keep REST for stable, high-volume, multi-client backend contracts, and add an MCP layer specifically where an AI agent needs to discover and select tools dynamically at runtime.
What is the core technical difference between MCP and a REST API?
MCP (Model Context Protocol) is a JSON-RPC 2.0-based protocol with a client-server model designed specifically for AI applications to discover and call tools, resources, and prompts at runtime. REST is an architectural style over HTTP for exposing fixed, versioned resource endpoints, and it has no built-in mechanism for an LLM to discover what operations exist or what their input schemas are.
Can an agent just call REST APIs directly instead of using MCP?
Yes, and many agent frameworks do this by wrapping REST endpoints (often via an OpenAPI spec) into tool definitions the model can call. The tradeoff is that someone has to write and maintain that wrapper per API, whereas an MCP server exposes its tools directly via a standard `tools/list` call that any MCP-compatible client can consume without custom glue code.
Is MCP a replacement for REST APIs?
No. MCP is commonly implemented as a thin layer in front of existing REST APIs, databases, or local system resources — translating them into agent-callable tools. REST continues to serve the underlying data/service layer and non-agent clients like web and mobile frontends.
When does REST clearly win over MCP?
REST is the better choice when the caller is deterministic application code (not an LLM deciding dynamically what to call), when the API must serve many client types beyond AI agents, or when you need the mature ecosystem of REST tooling — API gateways, caching, OpenAPI documentation, and established auth patterns like OAuth2.
When does MCP clearly win over raw REST for an agent?
MCP wins when an agent needs to work across many heterogeneous tools/data sources through one consistent protocol, when tool capabilities need to be discoverable and self-describing so the model can reason about what's available, or when connecting local resources (filesystem, git, a local database) via stdio transport without exposing a network-facing REST endpoint at all.