An operations lead asks an AI assistant to summarize last week’s content and prepare a draft for next week. The assistant can write fluent text, but it cannot see the calendar, the brand notes, or the results unless someone provides that context or connects it to a system.
That is where teams start hearing two terms: APIs and MCP. They are related, but they are not competing names for the same thing.
An API is an interface; MCP is a shared interaction protocol
An API is an interface a software system exposes so another program can request data or actions. HTTP APIs, for example, use defined request and response semantics to interact with resources. The details—available operations, fields, permissions, and errors—depend on the API’s design.
Model Context Protocol (MCP) is a protocol for connecting AI applications to external context and capabilities. Its specification defines interactions between MCP clients and servers, including server-provided tools, resources, and prompts. A tool can let an AI application request an action or retrieve information; a resource can provide context; a prompt can offer a reusable interaction template. See the MCP server concepts and the IETF HTTP Semantics specification for the underlying concepts.
In simple terms:
| API | MCP | |
|---|---|---|
| Main job | Let software interact with a particular system | Give AI applications a standardized way to discover and use exposed context and tools |
| Defined by | The API provider | The MCP protocol plus a particular server’s implementation |
| What it doesn’t guarantee | That every action is available to every caller | That a model is safe, authorized, accurate, or allowed to perform every possible action |
An MCP server may call an API behind the scenes. MCP doesn’t remove the need for APIs, credentials, authorization, or product-specific rules. It provides a common interaction layer for an AI application; the server still determines which capabilities it exposes and how requests are handled.
A protocol is not an agent or a permission grant
MCP does not itself supply the model, decide what the user meant, or grant access to a social account. An AI application can use MCP to discover tools that a server exposes, but a tool’s presence is not proof that it should be called in every situation. The server and connected system still need to enforce authorization and valid action boundaries.
For social media work, think about the difference between asking an assistant to read a content calendar and allowing it to change a scheduled post. Those actions have different consequences. An integration design should make the available operation, scope, authorization, and human review expectations clear.
A practical access ladder
When evaluating any AI connection to a social system, sort possible actions by consequence:
READ — retrieve information the user is allowed to see.
RECOMMEND — interpret information and suggest a possible next step.
PREPARE — create a draft or working material that a person can inspect.
ACT — change a live system, such as modifying, scheduling, or publishing content.
Risk generally rises as the system moves from reading toward changing real systems. The ladder is a planning framework, not a claim that every MCP server exposes all four levels. Teams should separately decide what credentials, confirmation steps, and review gates each operation requires.
Ask about the boundary, not just the connector
Before adopting an MCP or API integration, ask: Which data can it read? Which operations can it call? Who authorizes them? Can a person review a proposed change before it takes effect? What happens when the assistant is uncertain, receives stale data, or makes a malformed request? How can access be revoked?
Those questions matter more than the label on the connector. A familiar protocol does not make a broad permission safe by itself.
A short Markish perspective
At Markish, we see MCP and APIs as parts of a wider social media operations stack. The important design question is how tools, brand context, permissions, and human decisions fit together—not whether a protocol is fashionable. This article explains the industry concepts and does not claim that Markish currently provides autonomous social-media agents.
For the boundary between useful assistance and delegated action, read What Social Media Work Should AI Actually Automate?. The Custom MCP page describes Markish’s public MCP-related offering; this explainer is not a promise of autonomous publishing functionality.