If you have used Claude, ChatGPT, Cursor or GitHub Copilot recently, you have probably seen the term MCP. It stands for the Model Context Protocol, and it has quietly become the standard way AI assistants and agents connect to the tools and data a business actually runs on. This guide explains what MCP is in plain English, how it works, what it is good for, what the risks are and how companies are using it in 2027.
What Is MCP?
The Model Context Protocol is an open standard that lets AI applications connect to outside systems in a consistent way. Think of it as a universal adapter. Instead of every AI product building its own custom integration with every tool, a tool publishes one MCP server, and any AI application that supports MCP can use it.
Anthropic introduced MCP in November 2024. In December 2025 it donated the protocol to the Agentic AI Foundation, a fund under the Linux Foundation co-founded by Anthropic, Block and OpenAI, so no single company controls it. Today it is supported across most major AI products, including Claude, ChatGPT, Cursor, OpenAI Codex, GitHub Copilot and many agent frameworks.
Why MCP Matters
Before MCP, connecting an AI model to a CRM, a database or a ticketing system meant writing custom code for each model and each application. Five AI tools and ten business systems could mean dozens of separate integrations to build and maintain.
MCP turns that into a build-once problem. A company writes one MCP server for its CRM, and every MCP-compatible assistant or agent can use it. SaaS vendors such as Stripe, Notion, GitHub, Atlassian and HubSpot now publish official MCP servers so customers’ AI tools can work with their products directly.
How MCP Works
MCP has three main roles:
- Hosts are the AI applications people use, such as Claude, ChatGPT or Cursor.
- Clients live inside the host and manage the connection to each server.
- Servers expose a system’s capabilities to the AI.
A server can offer three kinds of things. Tools are actions the AI can take, such as creating a ticket or running a database query. Resources are data the AI can read, such as files or records. Prompts are reusable templates for common tasks.
When a host connects, it asks the server what it offers. The model then decides when to use a tool, the client sends the request, and the server does the work, usually by calling the system’s own API, and returns the result. Servers can run locally on a computer or remotely as a web service, and remote servers use an OAuth-based authorization flow so users sign in with their own accounts.
MCP vs APIs And Function Calling
MCP does not replace APIs. MCP servers usually call APIs underneath. It also builds on function calling, the model feature that lets an AI request a tool. What MCP adds is a shared, portable way to describe and deliver those tools to any AI client. Our comparison of MCP vs function calling vs APIs goes deeper.
What Businesses Use MCP For
- Sales and support: letting an assistant look up customers in the CRM, summarize tickets or draft replies with real account data.
- Engineering: giving coding agents access to GitHub, issue trackers, documentation and databases.
- Operations and finance: querying internal reports, reconciling records or preparing invoices.
- Product distribution: SaaS companies shipping an official MCP server so customers can use their product from inside Claude, ChatGPT or Copilot.
- Internal agents: connecting several in-house agents to the same approved systems through one governed layer.
Where To Find MCP Servers
The official MCP Registry is the canonical record of public servers and who publishes them, and directories such as GitHub’s registry, the Docker MCP Catalog, Glama and PulseMCP add search, quality signals and packaging. Our list of MCP directories and registries compares them, and our list of the best MCP servers picks the strongest servers by use case.
How To Start Using MCP
Most people start by connecting a ready-made server to a tool they already use. In consumer and business AI apps this is often a connector you switch on in settings. Developers can add servers to coding agents with a single command; our guide on how to install MCP servers in Claude Code, Cursor and Codex walks through it step by step.
If no server exists for an internal system, you can build one with the official SDKs or hire a specialist from our list of MCP development companies.
MCP Security Risks
MCP gives AI real access to real systems, so it needs the same care as any other integration, and a few new ones:
- Untrusted servers: a malicious or poorly written server can leak data or take unwanted actions. Prefer servers published by the vendor that owns the system.
- Prompt injection: text hidden in tool descriptions, documents or web pages can try to steer the AI into misusing tools.
- Over-broad permissions: a server that can delete records or move money should require approval for those actions.
- Credential handling: tokens should be scoped to each user and never exposed to the model.
Our guide on how to secure AI agents and MCP integrations, along with our lists of MCP security tools and MCP gateways, covers the controls companies put in place.
Where MCP Is Heading
The protocol now follows a formal release process under the Linux Foundation. The July 28, 2026 specification added features aimed at enterprises, including stronger authentication options, stateless server designs that are easier to scale and a deprecation policy that gives teams at least a year to adapt to changes. Expect more official vendor servers, more private registries inside large companies and more agents that discover and connect to tools on their own.
Frequently Asked Questions
Who Owns MCP?
MCP is an open-source project of the Agentic AI Foundation under the Linux Foundation. Anthropic created it and remains a contributor, alongside many other companies.
Is MCP Only For Claude?
No. ChatGPT, Cursor, OpenAI Codex, GitHub Copilot, Google’s tools and many open-source agents support MCP.
Is MCP Free?
The protocol and official SDKs are free and open source. Individual servers may require an account or paid plan with the vendor behind them.
Do I Need To Be A Developer To Use MCP?
No. Many AI apps let you turn on MCP connectors from settings. Building your own server does require development work.
Is MCP Safe?
It can be, with the right controls. Use trusted servers, limit permissions, require approval for sensitive actions and monitor what agents do.
Conclusion
MCP is the plumbing that lets AI assistants and agents work with real business systems. One server per system replaces dozens of one-off integrations, and support now spans almost every major AI product. For most companies, the right first step is connecting a few official servers to the tools teams already use, with security controls in place, and then building custom servers for the internal systems that matter most.
