MCP vs. API

Model Context Protocol (MCP) was proposed by Anthropic in November 2024 as a pluggable architecture for connecting AI agents to external systems and data. While MCP shares surface similarities with traditional APIs, it serves different purposes, is consumed by different clients, and follows a distinct lifecycle.

This post offers a high-level comparison of MCPs and APIs, focusing on the text modality, which is currently the most widely adopted.

What Problem Does MCP Solve?

Before MCP, AI agents were built using traditional software engineering patterns. Developers manually wired agents to external systems by implementing tools, crafting prompts, and managing context (e.g. via Retrieval-Augmented Generation). Any change — new tools, updated prompts, or data access modifications — required modifying agent logic.

MCP abstracts this integration work. It enables agents to discover and invoke tools, resources, and prompts dynamically. Instead of embedding integration logic in the agent, engineers can register modular, reusable MCP servers that agents understand semantically and contextually.

MCP Building Blocks

MCP defines three core primitives that expose data, actions, and prompts to AI agents in a modular and extensible way:

• Resources expose data (e.g., files, databases, logs) via URIs for use as LLM context.
• Prompts are reusable instruction templates or workflows that guide agent behavior.
• Tools let agents execute actions by invoking server-side functions or APIs.

With just resources, prompts, and tools, an MCP server can provide all the context an AI agent needs to reason and act without hardcoded logic.

Clients

MCPs are designed for AI agents and assistants. They are plug-and-play: once attached, the agent introspects available tools, resources, and instructions, and decides how and when to use them.

Humans can also benefit—MCP prompts help them instruct AI assistants in natural language, without needing deep technical integration.

In contrast, APIs are built for applications and services. They require developers to implement client logic, handle schemas, error handling, and version compatibility. Integrating a new API typically involves a full development cycle: design → develop → test → deploy.

Data Model

APIs define rigid contracts between client and consumer. These are strongly typed and validated against schemas (OpenAPI, GraphQL, etc.). Breaking changes require versioning, and incompatible updates can crash clients.

MCPs offer a contextual interface with a lightweight invocation schema. Output is typically unstructured or loosely structured text (e.g. JSON, Markdown, or code), designed to be interpreted by an LLM.

The agent decides — based on current context — which tools to invoke and in what sequence. This makes execution flow dynamic and adaptive.

Moreover, MCPs can self-update at runtime. New tools and resources can be registered or removed on the fly, and agents are notified of these changes.

Functional scope

APIs are typically designed to expose fine-grained, low-level operations. These include classical CRUD (Create, Read, Update, Delete) actions, data queries, status checks, and other specific business logic endpoints. They are precise, deterministic, and tightly coupled with the structure of the underlying service or data model.

MCPs, on the other hand, operate at a higher level of abstraction. Rather than exposing individual operations, they provide semantic tools and context for AI agents to interpret and act upon. A single tool in an MCP might wrap multiple API calls or complex workflows, with prompts that explain its use and expected outcomes. The focus is on enabling the agent to accomplish goals—such as analyzing text, retrieving relevant knowledge, or transforming datasets — without prescribing the exact sequence of operations.

Communication lifecycle

APIs are commonly stateless and scalable. Clients communicate with API via short-lived request/response flows. Bi-directional flows (e.g. long polling, webhooks, pub/sub) are used for handling of long running tasks or real-time apps.

MCP sessions are often stateful. An agent might interact with an MCP for minutes, hours, or days, maintaining context and evolving its task plan based on tool outputs and prompts.

Local MCP

One distinct feature of MCP is that it can be run locally on user’s machine. Such MCPs enables access to local services, such as databases, file systems, or other tools. This is a big deal, since it allows to run MCPs without any dedicated infrastructure, but requires user to configure each MCP and install additional runtime like python, nodejs or docker.

Authentication and authorization

Modern API offer several well established authentication methods auch as OAuth, JWT, API keys or session cookies. Authorization is handled by API server and is transparent for the client.

MPC authentication approaches are still in early stages. Localy running MCPs rely on process arguments or environment variables for providing authentication credentials and endpoint configuration. Remote MCPs offer OAuth or header-based authentication, that must be also supplied by the agent. When using OAuth, each MCP will require a separate authorization flow.

Summary

Aspect	API	MCP
Scope	Fine-grained operations	High-level, goal-oriented tasks
Clients	Apps and services	AI agents and assistants
Protocol	REST, GraphQL, gRPC, etc.	JSON-RPC or similar over HTTP/SSE/WebSocket
Schema	Strongly typed	Semantic, loosely structured
Lifecycle	Stateless, multi-client	Stateful, long-lived
Deployment	Hosted backend	Local process or remote MCP service
Dynamism	Static interface	Dynamic, capabilities added/removed at runtime
Output	Deterministic	Contextual, generative (Sampling or internal LLMs)

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Final Thoughts

MCP is not an evolution of the API — it’s a different layer altogether. APIs power applications. MCPs empower agents.

Where APIs offer structured access to logic and data, MCPs expose capability in a form that LLMs can reason about. Developers working with AI agents must shift their mindset from programming clients to providing intelligent contexts.