AI

What Is the Model Context Protocol (MCP)?

MCP is an open standard for connecting AI applications to external tools and data. Learn what it is, how it works, and why it matters for automation.

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The Model Context Protocol (MCP) is an open standard for connecting AI applications to external systems — tools, data sources, and workflows — through one consistent interface. Instead of building a custom connector for every service an AI needs to reach, developers build against MCP once and connect anywhere. The protocol's documentation describes it as "a USB-C port for AI applications": a universal connector rather than a different cable for every device (Model Context Protocol).

Why Does MCP Exist?

Large language models are isolated by default. They cannot read your files, query your database, or take an action in another system unless someone builds that capability for them. Before MCP, every one of those connections was bespoke.

IBM's explainer puts it plainly: LLMs on their own "cannot successfully run any user query that requires access to real-time information," and connecting external services was cumbersome enough that developers needed a standard (IBM). MCP is that standard. It converts tool output into a form the model understands, so integrations are plug-and-play rather than hand-wired.

Anthropic open-sourced MCP in November 2024 with the stated goal of replacing fragmented integrations "with a single protocol" (Anthropic). The agentic market it feeds is expanding quickly: Gartner projects that 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024 (Gartner).

How Does MCP Work?

MCP uses a client/server model with three parts:

  • The host — the AI application the user interacts with, such as Claude Desktop, ChatGPT, or an IDE.
  • The client — the component inside the host that turns user requests into structured protocol messages.
  • The server — the external system that exposes capabilities, such as Slack, GitHub, or a device fleet.

MCP servers expose three kinds of capability: resources (readable data), tools (actions with side effects), and prompts (reusable templates). Messages travel as JSON-RPC over a transport — local stdio or remote HTTP.

Is MCP Only for Developers?

No. The value for end users is delegation. Once a client is connected to a server, the user just asks in natural language. The model picks the right tool and calls it.

MCP's documentation lists everyday examples: an agent that reads your calendar and notes, a coding assistant that turns a design into an app, or a model that drives a 3D printer. The common thread is that the AI takes actions beyond talking — and MCP is the standard that makes those actions safe and repeatable to build.

Why Did MCP Become the Default So Fast?

Two things happened. First, it was open-sourced, so anyone could implement it. Second, the major AI vendors adopted it.

OpenAI added support for MCP across its products, including the ChatGPT desktop app, in March 2025 (TechCrunch). Anthropic's documentation now points to broad ecosystem support across Claude, ChatGPT, Visual Studio Code, and Cursor. When competitors converge on one protocol, it stops being a bet and becomes infrastructure.

The tooling market is tracking that shift. Postman's 2025 State of the API surveyed more than 5,700 developers, architects, and executives and devoted a chapter to the finding that "MCP is early, but it's gaining momentum" (Postman).

Is MCP a Replacement for APIs?

Not exactly. MCP sits on top of existing systems. An MCP server often wraps an API and exposes selected actions as tools. The difference is the audience: an API is built for developers writing code, while an MCP server is built for a model deciding which tool to call.

IBM makes the distinction clearly: MCP "does not decide when a tool is called and for what purpose." The model does. MCP standardizes the connection. For a deeper comparison, see MCP vs REST API.

How Conbersa Uses MCP

Conbersa runs managed, hardware-backed social distribution on a fleet of real physical phones. Its MCP server exposes that fleet to any MCP client, so an agent can post across accounts, run engagement, and schedule distribution in plain language. You can connect Claude, ChatGPT, Cursor, or opencode and be operating real accounts in minutes — see What is an MCP server? for the server side.

Neil Ruaro
Founder, Conbersa

We run agentic distribution on a fleet of real phones — and write up what we learn helping founders escape the cold start. Got a topic you want covered? Tell us.

FAQ

Frequently asked questions

MCP is an open standard that lets AI applications connect to external tools, data sources, and workflows through a single consistent interface, replacing one-off custom integrations. Its own documentation calls it a USB-C port for AI applications because one connector works across every client and server. Developers build once and reach many AI apps.
Anthropic created and open-sourced the Model Context Protocol in November 2024, publishing the specification, SDKs, and reference servers. It has since been adopted across the industry, including by OpenAI, which added MCP support across its products in March 2025. Broad client support now spans Claude, ChatGPT, Visual Studio Code, and Cursor.
Neither. MCP is a protocol and integration layer, not an agent framework. It defines how a client and server exchange tools, resources, and prompts, including transports and authentication. Agent frameworks decide when to call a tool and for what purpose; MCP standardizes how that call is made and how results come back.
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