What Is the Model Context Protocol (MCP)?

The Problem MCP Solves

Before MCP, every AI product needed its own custom integration for every business tool. Connecting an assistant to your CRM, your file storage and your ticketing system meant three bespoke connectors, and switching to a different AI app meant building them again. With many AI apps and many business tools, the number of integrations multiplied quickly.

MCP replaces that with a common protocol. A tool vendor (or your developer) builds one MCP server for a system, and any AI application that speaks MCP can discover and use it. The analogy people often use is a USB-C port for AI: one standard connector instead of a drawer of adapters.

How MCP Works

The official documentation describes three participants:

• Host: the AI application the person uses, such as Claude Desktop, ChatGPT, VS Code or a custom agent.
• Client: a component inside the host that keeps a connection to one server. A host with five connected servers runs five clients.
• Server: a program that exposes a system's capabilities. It can run on your own machine or as a remote service.

Servers offer three core kinds of things. Tools are actions the model can call, such as "create invoice" or "search tickets." Resources are data the app can read, such as a file or a database schema. Prompts are reusable templates for common tasks. Messages use JSON-RPC, a long-established format for remote calls. Local servers usually connect over standard input/output; remote ones use the Streamable HTTP transport, and the spec recommends OAuth for obtaining access tokens.

When the AI app connects, it asks each server what it offers. The model sees a list of available tools with descriptions and decides when to call them, and the host passes the results back into the conversation.

Who Supports MCP

Anthropic released MCP as an open specification in November 2024. Adoption spread across the industry quickly: Anthropic's December 2025 announcement lists ChatGPT, Cursor, Gemini, Microsoft Copilot and VS Code among the products that support it. That same month Anthropic donated MCP to the Agentic AI Foundation, a fund under the Linux Foundation co-founded by Anthropic, Block and OpenAI, with support from Google, Microsoft, AWS, Cloudflare and Bloomberg. The protocol continues to evolve through dated specification versions, so check the current spec at modelcontextprotocol.io rather than relying on older tutorials.

Many software vendors now publish official MCP servers for their products, and open-source servers exist for common systems like file storage, databases and developer tools.

What MCP Means for a Small Business

For most owners, MCP is plumbing you will not touch directly, but it changes a few decisions:

• Less lock-in. If your CRM or help desk has an MCP server, you can connect it to whichever AI assistant your team prefers and switch later without rebuilding.
• Cheaper custom integrations. If you need AI to work with an internal system, building an MCP server for it makes that system usable from several AI apps at once.
• Agents with real reach. MCP is a common way for AI agents to act across tools. See what an AI agent is and our guide to connecting AI to your CRM.

Security Questions to Ask

MCP makes it easy to give an AI broad access, which is exactly why it needs care. The protocol's own security best-practices page covers issues developers must handle. For a business owner, the practical checklist is:

• Who wrote the server? Prefer official servers from the software vendor or ones your developer has reviewed. A malicious or sloppy server can leak data or take unintended actions.
• What permissions does it have? Connect with an account scoped to what the task needs, read-only where possible.
• Does a person approve actions? Most MCP hosts can ask for confirmation before a tool runs. Keep that on for anything that sends, deletes or pays.
• What about untrusted content? If a tool reads emails or web pages, that text can contain instructions aimed at the model (prompt injection). Do not combine untrusted input and high-impact tools without review.
• Is activity logged? You want a record of which tools were called, by whom and with what inputs.

If you are planning MCP-based integrations, DR3AM AI automation can scope which systems are worth connecting and how to lock them down.