What Is MCP (Model Context Protocol)? A Business Leader's Guide to Connecting AI to Your Systems

The Model Context Protocol is an open specification for how an AI application talks to the systems around it. Without a shared standard, every pairing of an AI product and a business system needs its own connector: one for the CRM in ChatGPT, another for the CRM in Copilot, another for the ticketing tool in Claude. MCP replaces that grid of one-off integrations with a single interface. You describe a system once, as an MCP server, and any AI application that speaks MCP can discover what it offers and use it.

It is no longer a single-vendor project. Anthropic created MCP and, in December 2025, donated it to the Agentic AI Foundation, a directed fund under the Linux Foundation co-founded by Anthropic, Block and OpenAI, with support from Google, Microsoft, AWS, Cloudflare and Bloomberg. At that point Anthropic reported more than 10,000 active public MCP servers, 97M+ monthly SDK downloads across Python and TypeScript, and adoption in ChatGPT, Cursor, Gemini, Microsoft Copilot and Visual Studio Code. For a business, that neutrality matters more than the numbers: an integration built on MCP is not tied to one AI vendor.

The specification defines three roles. The host is the AI application a person uses, such as a chat assistant or a coding tool. Inside it, a client manages the connection to each server. The server is the piece that wraps one of your systems and exposes what it can do. Messages use JSON-RPC 2.0, and in the current revision (2026-07-28) every request is stateless and self-contained, with no protocol-level sessions.

A server can offer three kinds of things. The distinction is useful when you decide what to expose:

Yes. MCP does not replace your APIs; an MCP server usually sits on top of them. An API is designed for a developer who reads documentation and writes code against fixed endpoints. An MCP server is designed for a model that discovers tools at run time, reads their names and descriptions, and decides when to call them. That changes what good design looks like. A REST API with 80 endpoints can be fine for developers, but exposing all 80 as tools gives a model too many similar choices. A good MCP server offers a small set of task-shaped tools, with clear descriptions and input schemas, mapped to the jobs people actually ask the AI to do.

Most decisions about MCP fall into one of three patterns. They have different owners, costs and risks, so it helps to name which one you are discussing before anyone starts building.

The specification is explicit: MCP enables arbitrary data access and code execution paths, and "MCP itself cannot enforce these security principles at the protocol level." It asks implementers to obtain explicit user consent before exposing user data or invoking any tool, and the tools section says there should always be a human in the loop with the ability to deny tool invocations. It also warns that tool descriptions and annotations should be treated as untrusted unless they come from a trusted server. Those are requirements on the products you choose and the servers you build, not guarantees you get by adopting the standard.

The MCP security best practices document lists concrete failure modes: proxy servers that skip per-client consent (the confused deputy problem), servers that pass a user's token straight through to another API, server-side request forgery during OAuth discovery, guessable state handles, and local servers that run arbitrary commands on a laptop. It forbids token passthrough outright: MCP servers must not accept tokens that were not issued for that server. Add prompt injection on top. OWASP ranks it first in its 2025 Top 10 for LLM applications and notes that no method is known to prevent it fully, only to reduce its impact. Any document, email or web page an MCP tool returns can carry hidden instructions to the model.

The official MCP Registry, launched in preview in September 2025, is a catalog of publicly available servers built on self-reported information from maintainers. Moderation is reactive: the community flags problem entries and maintainers can remove them afterwards. Treat a listing like an app store entry before review, and run your own check. These are the questions we ask, drawn from the specification and its security guidance:

Pick one workflow where staff already copy information between an AI assistant and a business system, and connect that system with read-only tools first. Measure whether answers improve and how often people use it. Add write actions one at a time, each behind a confirmation step, and review the logs before widening access. If you plan to publish your own MCP server, treat it as a product: design a handful of task-level tools, implement OAuth properly with tokens issued for your server, and plan for change, because the specification is still evolving: the 2026-07-28 revision, for example, removed protocol-level sessions.