If you have been following artificial intelligence over the past year, three letters have started appearing almost everywhere: MCP.
It comes up in conversations about AI agents, ChatGPT, Claude, development tools, enterprise environments and increasingly the applications businesses already use every day.
Yet MCP is often discussed as though everyone already knows what it means.
The concept is actually fairly straightforward.
MCP, or Model Context Protocol, is a standardized way for AI systems to connect with other software, data and tools. The protocol itself is technical infrastructure for AI to talk to another application directly— think of it as a model-to-tool standard. MCP gives AI a common way to connect with the tools and information that power everyday work.
A simple architecture could look like this:
ChatGPT → MCP server → your tools/data
Claude → same MCP server → your tools/data
You could also build an MCP tool whose job is to call the Claude API. Then the flow becomes:
ChatGPT → MCP tool → Claude API → response back to ChatGPT
And the reverse could be built as well:
Claude → MCP tool → OpenAI API → response back to Claude
At its core, MCP creates a standard way for AI to work across the systems businesses already use.
Instead of building a completely different integration every time an AI model needs to communicate with a database, content management system, analytics platform or business application, MCP provides a common way for those systems to describe what information and capabilities are available.
The easiest way to understand its significance may be to think less about AI models and more about what happened to the web when common standards emerged.
A website can work in different browsers because they understand many of the same underlying standards. An API gives different applications a structured way to exchange information. MCP is attempting to establish something similar for the rapidly growing world of AI-powered applications.
And it happened remarkably quickly.
Where MCP Came From
Anthropic introduced the Model Context Protocol on November 25, 2024, releasing it as an open standard for connecting AI assistants with the systems where information actually lives.
The problem Anthropic was trying to solve was already becoming obvious.
Large language models were getting considerably more capable, but most business information remained outside of them. Documents lived in one platform. Customer information lived in another. Analytics existed somewhere else. Development teams had their own systems. Every new AI integration required another custom connection.
Anthropic described MCP as a way of replacing those fragmented integrations with a common protocol.
That alone would not have made MCP particularly important.
What changed was adoption.
During 2025, other major AI platforms and technology companies began supporting the protocol. OpenAI added support for remote MCP servers to its Responses API in May 2025, allowing developers to connect OpenAI models to tools exposed through MCP.
By December 2025, MCP had moved beyond being primarily an Anthropic-led project. Anthropic donated the project to the newly created Agentic AI Foundation under the Linux Foundation, establishing vendor-neutral governance. The foundation was created with contributions from Anthropic, Block and OpenAI, with support from organizations including Google, Microsoft, AWS, Cloudflare and Bloomberg.
MCP was no longer simply one AI company’s preferred integration method. It was becoming shared infrastructure that multiple competing platforms could use.
The protocol has continued evolving. The July 2026 specification introduced significant architectural and authorization improvements aimed at making MCP easier to scale and operate in production environments.
In less than two years, MCP moved from a newly introduced protocol to an increasingly common part of the infrastructure surrounding AI applications.
What Does an MCP Connection Actually Do?
Imagine asking an AI assistant:
“Find the three most visited service pages on our website, compare their content with our current positioning and identify anything that needs to be updated.”
The AI model itself probably does not have access to your analytics account or your CMS.
Traditionally, someone would have to build custom integrations connecting those systems to the AI application.
With MCP, those capabilities can be exposed in a standardized way.
One MCP connection might provide access to analytics.
Another might expose approved content from the CMS.
Another might provide your organization’s brand guidelines.
The AI application can discover what those connections allow it to do and use them while completing the task.
MCP currently organizes much of that interaction around several fundamental concepts, including resources, which provide information or context, and tools, which allow an AI system to perform an operation or retrieve information.
That distinction is important because AI becomes considerably more useful once it moves beyond simply generating text.
It can work with information that belongs to your organization.
And, where appropriate permissions have been established, it can take actions using your systems.
The MCP Server Is Essentially the Translator
One term you will hear frequently is MCP server.
That sounds more complicated than it needs to.
An MCP server is essentially the layer that tells an AI application:
Here is what this system contains. Here are the things you are allowed to ask for. Here are the actions available to you. And here is how you request them.
For example, an MCP server connected to a CMS might expose tools such as:
- search website content
- retrieve a page
- find pages containing a particular phrase
- create a draft
- retrieve metadata
- identify recently modified content
An analytics MCP server might expose completely different capabilities:
- retrieve traffic for a date range
- compare landing pages
- identify traffic changes
- retrieve conversion information
The AI application does not have to understand the proprietary architecture behind each system. It needs to understand MCP, while the MCP server handles the connection to the underlying software.
That is where much of the portability comes from.
Why Businesses Should Pay Attention
The most interesting part of MCP is not necessarily what it allows AI to do today.
It is what it potentially changes about how businesses build AI into their digital infrastructure.
Until recently, organizations frequently approached AI integrations around a specific model or vendor.
That can create another technology silo.
MCP encourages a different architecture.
Your organization can expose carefully defined capabilities through a common protocol, while different AI applications can potentially connect to those capabilities.
The AI model can change.
The interface can change.
The application using the connection can change.
The underlying business capability does not necessarily have to be rebuilt each time.
That separation could become increasingly important as organizations experiment with multiple AI models and agent platforms rather than committing every workflow to one system.
So How Could You Actually Use MCP?
The best place to start is not by asking, “Where can we implement MCP?”
Start with the work.
Look for situations where someone regularly has to move between several systems to answer one question or complete one task.
Consider a marketing team preparing a quarterly content review.
They may need to open analytics, search the website, check keyword performance, look through a content calendar and compare everything against current campaign priorities.
An MCP-enabled workflow could give an AI system controlled access to those different sources and allow someone to ask:
“Which of our high-traffic pages haven’t been meaningfully updated in the past 18 months, and which ones should we examine first?”
The value is not that AI generated another paragraph.
The value is that it could retrieve, organize and reason across information that previously required someone to manually assemble.
There are many similar opportunities.
A sales team might connect an AI application with CRM information, product documentation and approved case studies.
A development team might provide access to repositories, technical documentation and issue tracking.
A content team could connect website content, brand standards and performance data.
A digital product team could expose design-system documentation, component information and development standards so that AI-assisted work is based on the actual system rather than generic assumptions.
An organization with decades of institutional information could provide controlled access to internal knowledge without attempting to copy all of that information into every individual AI application.
The common thread is context plus capability.
Start With One Narrow Workflow
There is a temptation with emerging technologies to connect everything simply because it is possible.
MCP should probably be approached in the opposite way.
Start with one useful question.
Identify which systems are required to answer it.
Determine what information the AI genuinely needs.
Then decide what it should be allowed to do.
For an initial implementation, read-only access may be enough.
An AI system that can search, compare, summarize and identify information across several trusted sources can already remove considerable friction without being given permission to modify anything.
Actions can be added later.
That sequence also gives teams time to understand how people actually use the connection before introducing higher-risk operations.
Access Still Matters
MCP standardizes communication. It does not eliminate the need for security, governance or thoughtful system architecture.
Connecting an AI system to sensitive business information should not mean providing unrestricted access to everything an employee or application could theoretically reach.
Permissions still matter.
Authentication still matters.
User roles still matter.
The distinction between reading information and changing it matters considerably.
The MCP specification has continued developing authorization capabilities, including OAuth-based authorization and newer enterprise-focused approaches for centrally managing access.
Organizations should therefore think about MCP in much the same way they would think about any important integration layer: expose only what is necessary, define permissions deliberately and keep consequential actions visible to the people responsible for them.
MCP Does Not Replace Your Existing Systems
This may be the most important part to understand.
MCP is not another CRM.
It is not another CMS.
It does not replace your database, analytics environment, design system or internal applications.
And it does not automatically make poorly organized information useful.
If your content is inconsistent, permissions are unclear or systems do not have reliable underlying APIs, adding AI does not make those structural problems disappear.
MCP provides a cleaner way for AI applications to interact with the systems surrounding them.
The quality of those interactions still depends heavily on the quality of the digital ecosystem underneath.
In that sense, this is not only an AI conversation.
It is an information architecture conversation.
It is a systems conversation.
And increasingly, it is an experience design conversation.
From AI as a Destination to AI as a Layer
For the first several years of widespread generative AI adoption, much of the experience revolved around going somewhere to use AI.
Open a chatbot.
Ask a question.
Receive an answer.
MCP points toward a somewhat different model.
AI becomes a layer capable of working across the digital environments organizations already depend upon.
You may still interact with it conversationally, but behind that conversation could sit your content, applications, analytics, internal knowledge and operational tools—all accessible according to carefully defined rules.
That changes the role AI can play.
Instead of simply knowing things, it can increasingly work with your things.
And that may ultimately be why MCP has gained so much attention in such a short period of time.