What Is MCP? Model Context Protocol Explained Simply (2026)

Here’s something I’ve thought about a lot lately: AI assistants are incredibly smart, but they’re also weirdly isolated. Ask Claude or ChatGPT a question about something from their training data? Brilliant answer. Ask them to check your actual calendar or read a file from your computer? Suddenly, they’re stuck.

That’s the problem the Model Context Protocol—MCP—was built to solve. And honestly, I think it’s one of the most important developments in AI since large language models themselves.

If you’ve heard the term “MCP” floating around and wondered what all the fuss is about, you’re in the right place. I’m going to explain what MCP is, how it works, and why it matters—without drowning you in technical jargon. Whether you’re a developer curious about building with it or just someone who wants to understand where AI is heading, this guide has you covered.

Let’s dive in.

The Problem MCP Was Built to Solve

Before we talk about what MCP is, let’s talk about why it exists. Because understanding the problem makes the solution click.

Here’s the thing: Large language models like Claude 4, GPT-5, and Gemini 3 are trained on massive amounts of data. They “know” a lot. But there’s a fundamental limitation—everything they know is frozen at the time of training.

They can’t check the current weather. They can’t see what’s in your Google Drive. They can’t query your company’s database. They can’t book that flight for you. Not because they’re not smart enough, but because they’re isolated. Trapped inside their own world.

Now, developers found ways around this. They built custom integrations, APIs, and connectors. Need your AI to access Slack? Build a Slack connector. Need it to read from PostgreSQL? Build a database connector. Need it to check GitHub? You get the picture.

The problem? This creates what people call the “N×M integration nightmare.”

Imagine you have 10 AI applications and 10 different data sources. In theory, you’d need to build 100 different connectors—each one custom-made, each one needing maintenance, each one slightly different from the others.

This doesn’t scale. It’s expensive. It’s fragmented. And it means that AI assistants across different platforms have completely different capabilities depending on which integrations someone bothered to build.

MCP was created to fix this mess.

What Is MCP? The Plain-English Explanation

MCP stands for Model Context Protocol. It was introduced by Anthropic in November 2024 as an open-source standard for connecting AI applications to external tools and data sources.

The easiest way to understand MCP is through an analogy that’s become popular: MCP is like USB-C for AI.

Think about what USB-C did for hardware. Before it, you had different cables for different devices—different connectors for phones, tablets, laptops, cameras. USB-C gave us one universal standard. Now one cable works with almost everything.

MCP does the same thing for AI integrations. Instead of building custom connectors for every combination of AI application and data source, you build to the MCP standard. Once something speaks MCP, it can talk to anything else that speaks MCP.

So what exactly does “speaking MCP” mean? It means following a specific protocol—a shared language—for how AI systems request information, call tools, and receive data. It’s a set of rules that both sides agree to follow.

Here’s why the “open standard” part matters: Anthropic could have kept MCP proprietary, making it work only with Claude. Instead, they released it as open source. And in December 2025, they went even further—donating MCP to the newly formed Agentic AI Foundation (AAIF), which operates under the Linux Foundation.

This means MCP doesn’t belong to any single company. It’s a shared standard that OpenAI, Google, Microsoft, and others have all agreed to support. That’s huge. It means your MCP integrations work across the entire AI ecosystem, not just one platform.

If you want to understand what AI agents are and why they need external connections, MCP is a fundamental piece of that puzzle. Agents can’t be truly autonomous if they can’t access real-world data and tools.

How MCP Works: Client-Server Architecture Simplified

Alright, let’s get into how MCP actually works. Don’t worry—I’ll keep this approachable.

The Three Players: Host, Client, Server

MCP uses a client-server architecture, but with a slight twist. There are three key players:

The Host is your main AI application. Think Claude Desktop, an AI-powered IDE, or any app that has an AI assistant built in. The host is what you interact with directly.

The Client lives inside the host. It’s the intermediary that handles the actual MCP connections. When the AI needs to reach out to an external system, the client manages that communication—establishing connections, negotiating protocols, routing messages.

The Server is a lightweight program that exposes specific capabilities. Here’s the key insight: each MCP server typically wraps one external service. So you might have a GitHub MCP server, a PostgreSQL MCP server, a Google Drive MCP server, and so on.

When you want Claude Desktop to access your files, here’s what happens behind the scenes:

Claude (the host) decides it needs file access
The MCP client connects to the Filesystem MCP server
The server provides file operations (read, write, list files)
Claude can now work with your local files

The beauty is that Claude doesn’t need to know the details of how file systems work. It just knows how to talk MCP, and the server handles the rest.

The Three Primitives: Tools, Resources, Prompts

MCP servers can expose three types of capabilities (called “primitives”):

Tools are actions the AI can take. Think of them as functions the AI can call. “Send this email,” “Create this file,” “Run this database query”—these are all tools.

Resources are data the AI can read. These are like read-only access points. “Here are the contents of this document,” “Here’s what’s in this database table,” “Here are the files in this folder.”

Prompts are reusable conversation patterns. These are less commonly discussed but quite powerful—they let servers provide pre-built prompt templates for common tasks.

Most people focus on tools and resources, and that’s fine. Those are the workhorses that make AI assistants actually useful.

The Technical Bits (Simplified)

For the technically curious: MCP uses JSON-RPC 2.0 for communication. If that means nothing to you, just know it’s a widely-used, lightweight protocol for sending structured messages back and forth.

MCP supports different transport mechanisms:

STDIO for local connections (when the server runs on your machine)
Streamable HTTP for remote connections (when the server is somewhere on the internet)

The design was heavily inspired by the Language Server Protocol (LSP), which revolutionized how code editors work with programming languages. If LSP is how editors understand code, MCP is how AI understands external tools.

MCP in Action: Real-World Examples

Theory is great, but let’s see what MCP actually does in practice. Here are some concrete examples that show why this matters.

Example 1: AI Reading Your Files and Documents

With a Filesystem MCP server, Claude Desktop can read and write files on your computer. You could say “summarize the PDF in my Downloads folder” and Claude would actually go read that file, not ask you to paste the contents.

I’ve found this incredibly useful for working with documentation—instead of copying and pasting, the AI can just access what it needs directly.

Example 2: AI Querying Databases Directly

With a PostgreSQL or SQLite MCP server, an AI assistant can run database queries for you. You might ask “what were our top-selling products last month?” and the AI can query your database, get the actual data, and analyze it—all without you writing SQL.

This is where MCP starts getting powerful for business applications.

Example 3: AI Managing GitHub Repositories

The Git and GitHub MCP servers let AI assistants interact with your code repositories. Review this pull request. Show me the recent commits to this project. Create an issue for this bug. These become conversations, not manual tasks.

For developers, this kind of integration with LangChain agents and other agentic frameworks is becoming increasingly common.

Example 4: AI Automating Business Workflows

When you connect multiple MCP servers, things get really interesting. Imagine an AI assistant that can:

Check your calendar (Google Calendar MCP server)
Read your task list (Notion MCP server)
Send messages (Slack MCP server)
Create support tickets (Jira MCP server)

Suddenly you have an AI that can actually coordinate work across your tools, not just talk about it.

Why This Matters for Non-Developers Too

Even if you never write code, MCP matters to you. It’s the infrastructure that will make future AI assistants genuinely useful—able to do things in the real world, not just answer questions.

The AI assistant that handles your email, schedules your meetings, and manages your files? That’s built on protocols like MCP. The quality of your AI experience increasingly depends on these connections.

The MCP Ecosystem in 2026

MCP has come a long way since its November 2024 launch. Here’s where things stand now.

Who’s Using MCP Now

Anthropic (Claude) - As the creators, they’ve baked MCP support deeply into Claude Desktop and their API. Claude was built for MCP from the ground up.

OpenAI (ChatGPT) - In March 2025, OpenAI officially adopted MCP for their ChatGPT desktop application. This was a major validation of the protocol—OpenAI could have pushed their own standard, but they chose to join MCP instead.

Google (Gemini) - Announced support in May 2025. Google has since launched fully managed MCP servers for services like Maps, BigQuery, GCE, and GKE. They’re treating MCP as production infrastructure, not an experiment.

Microsoft and GitHub - Both joined MCP’s steering committee at Microsoft Build 2025. GitHub’s integration means coding assistants across the industry can use the same MCP servers.

Red Hat - Integrating MCP into OpenShift AI for enterprise deployments.

The point is: this isn’t one company’s proprietary technology. All the major players are in.

The Agentic AI Foundation

In December 2025, Anthropic donated MCP to the newly formed Agentic AI Foundation (AAIF), which operates as a directed fund under the Linux Foundation.

The AAIF was co-founded by Anthropic, Block, and OpenAI, with additional support from Google, Microsoft, AWS, Cloudflare, and Bloomberg. That’s essentially everyone who matters in enterprise AI.

Why does this matter? It guarantees MCP’s future. It can’t be abandoned, acquired, or made proprietary. It’s now a shared industry standard with governance that doesn’t depend on any single company.

For a deeper look at how to work with Claude’s API, check out our Claude API tutorial.

Popular MCP Servers You Should Know About

The MCP ecosystem has exploded. There are thousands of community-built servers now, but here are the ones worth knowing about:

Filesystem Server - Secure file operations with configurable access controls. This is the foundation for any AI that works with local files.

Git Server - Tools to read, search, and manipulate Git repositories. Essential for development workflows.

Memory Server - A knowledge graph-based persistent memory system. This lets AI assistants remember things across conversations—crucial for building more capable agents.

Database Servers - PostgreSQL, SQLite, ClickHouse, and others. These turn AI assistants into data analysts who can query your actual data.

Fetch Server - Web content fetching and conversion for efficient LLM use. When AI needs to read a webpage, this handles it.

Sequential Thinking - Supports dynamic, reflective problem-solving through thought sequences. This is interesting for complex reasoning tasks.

Business Tool Servers - Slack, Google Drive, Notion, Asana, Linear, Jira—there are MCP servers for most major productivity tools. The ecosystem is expanding rapidly.

Specialized Servers - Blender for 3D modeling, Figma for design, Spotify for music control, Puppeteer for browser automation. The variety is remarkable.

Where can you find more? The official Model Context Protocol server directory is the best starting point. GitHub searches also surface many community servers.

MCP vs Function Calling: What’s the Difference?

If you’ve used AI APIs, you might know about “function calling” or “tool use”—features where LLMs can request to call external functions. So how is MCP different?

Function calling is typically vendor-specific. OpenAI has their way of defining functions, Anthropic has theirs, Google has theirs. If you build tools for one, you need to adapt them for others.

MCP is a standardized protocol that sits above vendor-specific implementations. Build an MCP server once, and it works with any AI system that supports MCP—regardless of which company built that AI.

Here’s another difference: function calling is usually about single interactions. You define a function, the AI calls it, you return a result. MCP is designed for richer, ongoing connections. An MCP server can maintain state, provide multiple tools and resources, and handle more complex interactions.

They’re not mutually exclusive, though. Many AI systems use their native function calling internally but can also connect to MCP servers. Think of MCP as a superset that provides interoperability.

When should you use which? If you’re building something quick and only need it to work with one AI provider, function calling might be simpler. If you want your integration to work across the ecosystem and potentially handle more complex scenarios, MCP is the way to go.

Learning to build AI agents often involves understanding both approaches and when each makes sense.

Getting Started with MCP: Your First Steps

Ready to try MCP yourself? Here’s how to get started, depending on your technical level.

For Non-Developers: Using Claude Desktop with MCP

The easiest way to experience MCP is through Claude Desktop. Anthropic’s desktop app has MCP support built in, allowing you to connect various servers without writing code.

Download Claude Desktop if you haven’t already
Go to Settings → Integrations
Add MCP servers (many are pre-configured for easy setup)
Start using Claude with your new capabilities

Common starting points include file access, web fetching, and note-taking integrations.

For Developers: SDKs and Building Servers

If you want to build with MCP, there are official SDKs available in:

Python
TypeScript
C#
Java

The Python and TypeScript SDKs are the most mature and widely used. The MCP documentation provides excellent getting-started guides.

Building a basic MCP server isn’t that complex. You define your tools and resources, implement the handlers, and run the server. A simple server can be just a few dozen lines of code.

For those already familiar with AI APIs, our OpenAI API tutorial provides good foundational knowledge that translates well to MCP development.

Security Considerations

A quick word on security: MCP servers can have significant access to your systems. Before connecting to any MCP server, consider:

Trust: Only use servers from sources you trust. Community servers are great, but verify what they do.
Permissions: Many servers let you configure what they can access. Use the principle of least privilege.
Remote servers: Be especially careful with remote MCP servers. They’re processing your requests elsewhere, so authentication and encryption matter.

The good news is that MCP was designed with security in mind. It supports authorization mechanisms and can be locked down appropriately for enterprise use.

What’s New in MCP for 2026

MCP continues to evolve. Here are the notable developments this year:

Sampling: Bidirectional Communication

The 2026 update introduced “Sampling,” which enables bidirectional communication. Previously, communication was one-way: the AI asks, the server responds. With Sampling, servers can now request LLM completions from the host.

Why does this matter? It enables more sophisticated interactions. A server could ask the AI to analyze something, wait for the result, then take action based on that analysis. This opens up new architectural patterns for building AI systems.

Google’s Managed MCP Servers

Google launched fully managed MCP servers for services like Maps, BigQuery, GCE, and GKE. This treats MCP as production infrastructure with SLAs, reliability, and enterprise features.

This is significant because it signals that MCP is moving from “developer experiment” to “production-ready infrastructure.”

Expanding Enterprise Adoption

Red Hat’s integration into OpenShift AI is one example, but we’re seeing MCP adoption across enterprise environments. Companies are building internal MCP servers for their proprietary systems, creating standardized ways for AI to interact with company data.

Skills: The Next Layer

Building on MCP’s success, a concept called “Skills” is emerging: tiny Standard Operating Procedures (SOPs) that detail how an AI tool should perform specific actions. Think of Skills as workflows built on top of MCP primitives.

This represents the stack maturing—MCP provides the connectivity, Skills provide the expertise.

Frequently Asked Questions

Is MCP free to use?

Yes, completely. MCP is open source under a permissive license. You can use it, build with it, and deploy it without paying anyone. The official SDKs and documentation are freely available.

Do I need to code to use MCP?

Not necessarily. Apps like Claude Desktop let you use MCP servers without writing code—you just configure connections. However, if you want to build custom MCP servers or deeply customize behavior, some programming knowledge helps.

Is MCP only for Claude, or does it work with other AI?

MCP works with any AI system that supports it. That now includes ChatGPT, Gemini, Copilot, and many others. The whole point of MCP is interoperability—build once, work everywhere.

Is MCP secure?

MCP was designed with security in mind and supports authentication and authorization. However, security depends on implementation. Official and well-maintained servers follow best practices, but you should be cautious with unknown third-party servers, just as you would with any software.

What’s the difference between MCP and APIs?

APIs are how you talk to a specific service. MCP is a standardized way for AI systems to discover and use many services. Think of it as a layer of abstraction—MCP servers often wrap APIs to make them accessible to AI in a consistent way.

Looking Ahead: Why MCP Matters for AI’s Future

We’re at an interesting inflection point. For years, AI models got smarter—better at understanding language, generating text, reasoning through problems. But they remained fundamentally isolated, unable to interact with the real world except through clunky workarounds.

MCP changes that. It provides the infrastructure for AI to reach out, access data, take actions, and maintain context across systems. It’s the connective tissue that enables AI assistants to be genuinely useful rather than impressively knowledgeable but ultimately disconnected.

And because MCP is an open standard with industry-wide adoption, we’re not waiting for one company to build all the integrations. The ecosystem is growing organically, with thousands of developers contributing servers for their tools and platforms.

If you want to understand where AI is heading—toward agents that can do real work, not just answer questions—understanding MCP is essential.

Ready to explore? Start by trying Claude Desktop with a few MCP servers. Experiment with file access or web fetching. If you’re a developer, pick up the Python or TypeScript SDK and build something simple. The best way to understand MCP is to use it.

The future of AI is connected. MCP is how we get there.