Generative AI tools are impressive, but I’ve long argued that they’re not very useful in the real world unless they have access to more information than their training data—and can actually do something with it. It’s this ability that allows AI tools to create actionable content, offer useful insights, and perform actions that actually move work forward.

The Model Context Protocol (MCP) is a way to provide AI models with the context they need and allow them to perform real actions in other apps.

So let’s take a closer look at what MCP is, how it works, and why it matters.

Table of Contents:

What is MCP?

The MCP is a two-way communication bridge between AI assistants and external tools, providing access to information, but more importantly, the ability for AI to take action. It was Originally developed by Anthropicbut at this point almost every AI platform has accepted it.

This is one Open source A protocol designed to safely and securely connect AI tools to your company’s CRM, Slack workspace, or dev server. This means your AI assistant can pull relevant data and trigger actions in these tools such as updating a record, sending a message, or terminating a deployment. By empowering AI assistants to both understand and act, MCP enables more useful, context-aware, and proactive AI experiences.

Let’s look at an example. If you connect ChatGPT to Slack’s MCP server, you can tell ChatGPT to search your Slack for something and use that information to respond to you. You can also ask ChatGPT to send a message to Slack on your behalf. All without leaving the ChatGPT interface. And you don’t need to wire up a bunch of individual MCP servers because Zapier MCP Lets you connect to 9,000+ apps with one connection.

How does MCP work?

MCP is a standard framework that defines how AI systems can interact with external tools, services and data sources. Instead of creating custom integrations for each service, MCP defines the basics of how they should interoperate, how applications are structured, what features are available, and how they can be discovered. It enables developers to easily and reliably create secure, two-way connections between AI tools and external data sources, apps and other services.

People like to compare it to USB-C—a single cord that can connect to your phone, laptop, iPad, and even that fancy new immersion blender you got.

Another analogy is the World Wide Web. Hypertext Transfer Protocol (HTTP) defines how browsers and apps interact with websites and web servers. You can connect to zapier.com using Chrome, Safari, or even your Terminal app since they all use HTTP. MCP is an attempt to create an HTTP-like protocol for AI interoperability — it gives AI tools a common protocol to use.

Of course, this doesn’t quite capture the whole picture because AI tools aren’t actually like web browsers. They are capable of understanding language and intent, so MCP is designed to provide AI models with a structured set of options from which to choose. If you have an MCP server capable of downloading web pages from the Internet, the AI ​​model should be able to request it whether you say “go to zapier.com,” “take me to zapier.com” or something similar. And if you say “get me photos of my dog”, he should know to use Google Drive instead.

Client-Host-Server Model of MCP

Now, let’s look at more subtleties. MCP works using a client-host-server model:

• MCP host— In general, a Chatbot, IDEor other AI tool—is the central coordinator within the application. It’s ChatGPT, Claude, Cursor, or any other AI tool you’re spending your time on. Depending on how things are configured, the host may decide to invoke something on the MCP based on your request or based on an automated process.

• MCP Client is initiated by the host and connected to a server; It handles communication between the Host and Server.

• MCP server Connects to a data source or tool, local or remote, and exposes specific capabilities. For example, an MCP server connected to a file storage app can provide capabilities like “Find file” and “Read file”, while an MCP server connected to your team chat app can provide capabilities like “Get my latest mentions” and “Update my status”. Most business apps currently have MCP servers (or if you’re a developer, you can write your own).

MCP servers can deliver data using three basic methods:

• Indicates. There are default templates for LL.M which the user can select through slash commands, menu options and so on.

• Resources There is structured data, such as files, data from databases, or commit histories, that provide additional context to the LLM.

• tools They are functions that allow the model to take action, such as interacting with someone API or writing something to a file.

Although MCP is superficially similar to the way APIs work, the two differ significantly in design, intent, and flexibility. one API offers a straightforward, service-specific interface, while MCP is designed to be a unified framework. Many MCPs use server APIs when they trigger on an MCP, so the two often work together—but they’re not the same thing.

What problem is MCP solving?

AI tools are only as useful as the data they have access to and the actions they can take.

For general queries, LLM training data or web search will suffice. But if you want an AI tool like ChatGPT or Claude to know how your company’s sales figures compared to last quarter, how your competitor’s marketing has changed in response to market conditions, or just what your CEO’s email address is, you need a way to provide it with relevant information.

And if you want AI. do it To do something with that information—like send a report, create a task in your project management tool, update a record in your CRM, or notify your team on Slack—you need a way to interact with these apps. MCP makes this easy by providing a standard way to discover and execute AI tools in external systems. It bridges the gap between understanding and action, so AI isn’t just responding intuitively — it’s acting proactively.

For example, with Implementation of Zapier’s MCPyou can trigger actions in all your work apps directly from your favorite chatbot or AI coding agent. This means your AI tools aren’t limited to answering questions—they can take action, like sending an email, creating a task, or updating a record. You’ll never leave your chatbot or agent, but your other apps will still work.

First, this means building custom integrations for each app you want to get insights from or take action on. anyone Data source Any app that supports MCP is able to offer a structured set of tools or actions that an AI assistant or agent can take advantage of. When you ask an AI to do something, it can check what tools it has available and take the appropriate action—it’s very flexible.

By standardizing communication between AI models and external data sources with a secure protocol, MCP makes secure integration with key tools much faster and easier, and it also makes it easier to exchange between different tools.

How to Get Started with MCP

If you want to use your AI chatbot or agent to take action—not just access data—MCP is worth a look. It gives your AI tools the ability to communicate with external systems, such as sending messages, creating records, or initiating workflows, through a standard protocol.

It is the easiest option to start with. Zapier MCP. With a few clicks, you’ll have access to any of the 9,000+ apps available through Zapier. Take a look at some examples of what it looks like. Cursor, Open Claw, Codex, copilot, Chat GPTand Claudeor LEarn more about How to get started with Zapier MCP.

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This article was originally published in April 2025. The latest update was in May 2026.