Build a REST-style HTTP API as the reusable application interface for general software clients; add an MCP server when MCP-capable AI applications need to discover and invoke selected tools or access contextual resources. In many systems, both belong: the API remains the service boundary, while MCP provides an AI-facing adapter.
What is the difference between MCP and a REST API?
They solve related but different integration problems. Model Context Protocol (MCP) is designed to connect AI applications with systems that provide data and tools. Its server can expose three kinds of capabilities:
- Tools: executable functions an AI model can use to take an action or retrieve information.
- Resources: contextual data managed by the application or client.
- Prompts: reusable templates or instructions. MCP distinguishes control: prompts are user-controlled, resources application-controlled, and tools model-controlled.
HTTP is a stateless request/response protocol with standardized methods and a uniform interface. REST is an architectural style; in everyday use, “REST API” often means an HTTP API. RFC 9110 defines HTTP semantics and methods.
The terms are not alternatives at the transport level. Remote MCP uses HTTP too; MCP adds its own protocol methods, capability model, and interaction conventions. An OpenAPI specification describes HTTP API operations and security schemes for general-purpose clients and tools. It is a different contract from MCP’s AI-oriented tools, resources, and prompts.
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Should you build an MCP server or a REST API?
| Choose | When it fits | What it gives callers |
|---|---|---|
| REST-style HTTP API | Many kinds of clients need stable access to application resources and operations; existing API consumers, HTTP infrastructure, or OpenAPI tooling are important; or the service interface should remain useful independently of a particular AI host. | Resource- and operation-oriented HTTP semantics, with an API contract that general software clients can use. |
| MCP server | The intended consumers are MCP-capable AI applications, and the integration benefits from a discoverable, curated set of model-usable capabilities. | Tools, contextual resources, and reusable prompts designed for the MCP client/server relationship. |
| Both | An API exists or should be the stable service boundary, while AI applications also need an MCP-native interface. | A reusable application API plus an MCP layer that exposes selected capabilities to AI hosts. |
There is no documented universal winner on implementation cost, speed, adoption, or success rate. Treat the choice as an architecture decision based on callers and requirements, not a claim that one protocol is inherently cheaper or faster.
Can you use MCP with an existing REST API?
Yes. A practical design is to keep the HTTP API as the underlying application interface and build an MCP server that wraps selected operations. The adapter can translate API capabilities into narrower tool schemas and expose relevant data as resources or reusable prompts where appropriate. This is an architectural recommendation based on the roles of the two interfaces, not a requirement imposed by MCP.
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Avoid exposing every endpoint automatically. Choose operations that make sense for a model to call, give each tool a clear purpose and bounded inputs, and preserve the API’s authentication and authorization rules behind the adapter. That lets non-AI clients continue to use the API without depending on a particular MCP host.
What to compare before choosing
- Who will call it? List browser and mobile clients, internal services, third-party software clients, MCP hosts, or the mix you actually need to support.
- What interface do they need? Choose resource-oriented HTTP operations and an API contract for general clients; choose MCP tools, resources, and prompts for model-facing interaction.
- What can you reuse? Existing endpoints and OpenAPI documentation may help inform an MCP adapter. Available sources do not quantify the implementation savings, so validate effort in your own system.
- Where do authority and credentials come from? Decide whether calls use user-delegated or service credentials, define scopes and tenant isolation, and restrict which actions a model can invoke. The MCP authorization guidance and OAuth 2.0 security best current practice are relevant to that design.
- How will it run? Account for routing, caching, observability, and whether application state must persist between calls. Do not assume a stateless protocol means your application has no state.
- Which versions must interoperate? Pin down the MCP specification revision, host clients, and SDKs you intend to support before relying on newer behavior.
What changed in MCP’s 2026-07-28 release?
The official 2026-07-28 release announcement describes a stateless protocol core for that revision. It removes the protocol-level initialize/initialized exchange and Mcp-Session-Id; a server/discover call can optionally obtain capabilities. These are revision-specific changes, not assumptions to apply to older clients or specifications.
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The release also specifies required Mcp-Method and Mcp-Name headers on Streamable HTTP requests for routing and metering. Where an application needs state across calls, the release recommends explicit server-minted handles passed as ordinary tool arguments. Application state can still exist even when the protocol core is stateless.
Authorization changes include issuer validation for authorization servers and a shift away from Dynamic Client Registration toward Client ID Metadata Documents (CIMD), while retaining backward compatibility for now. Protocol adoption does not secure an implementation by itself: validate tokens, limit permissions, and define which operations tools may perform.
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The MCP TypeScript SDK v2 documentation identifies v2 as the stable release line implementing the 2026-07-28 specification and describes building servers that expose tools, resources, and prompts to MCP hosts. Client and SDK support varies; confirm the versions in your target stack before implementation.
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How to make the decision
- If broad software-client compatibility is the primary goal, start with an HTTP API and document its contract.
- If AI hosts are the primary consumers, expose a deliberate MCP surface rather than treating a generic endpoint as an AI integration by default.
- If you need both, keep the application API reusable and place MCP at the AI boundary, exposing only the capabilities those hosts need.
- Before committing, validate the design with a small implementation against your real API, authorization model, host clients, and deployment environment.
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