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What is MCP (Model Context Protocol)?

Illustration of the Model Context Protocol: one shared connector linking AI applications to external tools and data, replacing many custom integrations.

MCP, the Model Context Protocol, is a standard way to connect AI models to external tools and data. Instead of building a custom integration for every combination of AI system and outside service, you build to one shared protocol, and any compliant AI application can use any compliant tool. It is often described as a “USB-C for AI”: one universal connector in place of a drawer full of incompatible cables.

MCP was created by Anthropic, first released in late 2024, and it is where Claude connects to the outside world. But it has grown into something bigger than any one company, which is part of what makes it worth understanding.

The problem it solves

Once AI models could use tools, a messy problem appeared. Every time you wanted to connect a model to a service, a database, a file system, a business app, someone had to build a custom bridge for that specific pairing. With many models and many tools, the number of integrations explodes: connect M models to N tools the old way and you face roughly M times N separate bridges to build and maintain. This is sometimes called the NxM problem, and it does not scale.

MCP turns that multiplication into addition. A tool’s owner builds one MCP server for it, once. An AI application builds one MCP client, once. After that, any client can talk to any server through the shared protocol. The NxM tangle of custom bridges becomes a manageable N plus M, which is exactly why the idea caught on so quickly.

How it works

The design is deliberately simple, with two roles and a shared language between them.

Servers expose capabilities. An MCP server sits in front of a tool or data source and describes, in the protocol’s standard format, what it offers: data it can provide, actions it can perform, and prompt templates it supports. The team that owns a system builds its server once, and it is then available to any MCP-compatible AI.

Clients connect and use them. An AI application, the client, connects to any MCP server and can immediately use what it exposes, without custom code for that specific tool. The AI model can then read data or take actions through the server as part of answering.

It builds on tool use, it does not replace it. MCP is not a competitor to the tool-use capability models already have; it standardizes how that capability is wired up. Under the hood, the model still decides to use a tool and the application still executes it. MCP simply provides a consistent, reusable way to connect the two, so integrations stop being one-off engineering projects.

One important point for understanding the wider landscape: although MCP began at Anthropic, it did not stay proprietary. It was released as an open standard, and in late 2025 Anthropic handed its stewardship to a vendor-neutral foundation. By 2026 it had been adopted well beyond Claude, including by other major AI providers, becoming a shared industry standard rather than one company’s feature. It lives under Claude on this site because that is its origin and where Claude uses it, but the concept is not Claude-only.

A concrete example

Suppose a company wants its AI assistants to work with its Notion workspace, its Slack, and its internal database.

The old way, each AI tool needing that access required its own three custom integrations, rebuilt for every new assistant. With MCP, the company builds three servers, one for Notion, one for Slack, one for the database, each once. Now any MCP-compatible assistant, today’s or next year’s, can use all three immediately. The integration work happened once per system instead of once per pairing, which is the whole point.

How it connects

MCP is the standardized plumbing for tool use, and it becomes especially valuable for an agent, which may need to reach many tools and data sources as it works. In practice it is also how environments like Claude Code connect to external systems.

For the technical roles it is core infrastructure. An AI Solutions Architect decides when a shared protocol beats bespoke integration, and an AI/LLM Developer builds the servers and clients that make it real.