If you’ve been following developments in AI, you’ve probably come across the term MCP — short for Model Context Protocol. It’s been gaining serious traction as a way to standardise how AI models interact with external tools, data sources, and services.
But what exactly is MCP, and why does it matter? Let’s break it down.
MCP in One Sentence
The Model Context Protocol is an open standard that defines how AI models connect to and communicate with external systems — things like databases, APIs, file systems, and web services.
Think of it as a universal adapter. Just as USB-C gives you one port that works with countless devices, MCP gives AI agents one protocol that works with countless tools and data sources.
Why Was MCP Created?
Before MCP, every AI integration was custom-built. If you wanted an AI assistant to check your calendar, search your documents, and query a database, each of those connections required separate, bespoke code. This led to:
- Fragmentation — every AI platform had its own way of connecting to tools
- Duplication — developers rebuilt the same integrations over and over
- Brittleness — custom connections broke easily and were hard to maintain
MCP solves this by providing a shared language that any AI model can use to interact with any compatible service. Build the connection once, and it works across different AI systems.
How Does MCP Work?
MCP follows a client-server architecture with three main components:
1. MCP Hosts
These are the AI applications — things like Claude, ChatGPT, or your own AI-powered app. The host is where the AI model runs and where users interact with it.
2. MCP Clients
Clients live inside the host application and manage the connection to MCP servers. They handle the protocol details — sending requests, receiving responses, and maintaining the communication session.
3. MCP Servers
Servers are lightweight programs that expose specific capabilities. An MCP server might provide:
- Tools — functions the AI can call (e.g., “search the web”, “send an email”, “query a database”)
- Resources — data the AI can read (e.g., files, database records, API responses)
- Prompts — pre-built templates that guide the AI’s behaviour for specific tasks
When an AI agent needs to do something — like look up information or take an action — it sends a standardised MCP request to the appropriate server, which processes it and returns the result.
A Practical Example
Imagine you’re using an AI coding assistant. With MCP, that assistant could:
- Read your project files via a filesystem MCP server
- Search documentation via a web search MCP server
- Query your issue tracker via a Jira or GitHub MCP server
- Run your tests via a terminal MCP server
All of these interactions happen through the same protocol. The AI doesn’t need custom code for each tool — it just speaks MCP.
MCP vs Traditional APIs
You might be wondering: how is this different from a regular API?
| Aspect | Traditional APIs | MCP |
|---|---|---|
| Designed for | App-to-app communication | AI-to-service communication |
| Discovery | Manual — read docs, write code | Automatic — AI discovers available tools |
| Context | Stateless or session-based | Rich context management built in |
| Interaction | Request-response | Bidirectional, with streaming support |
| Flexibility | Fixed endpoints | Dynamic tool and resource discovery |
The key difference is that MCP is built specifically for AI agents. It includes features like tool discovery (the AI can ask “what can you do?”), context management (maintaining relevant state across interactions), and capability negotiation (agreeing on what’s supported).
For a deeper comparison, check out our post on MCP vs traditional approaches.
Who’s Behind MCP?
MCP was introduced by Anthropic — the company behind Claude — and released as an open standard. This means anyone can implement it, build MCP servers, or integrate MCP into their applications.
The open nature is important. It means MCP isn’t locked to one AI provider. A server built for Claude can also work with other AI systems that support the protocol, creating a growing ecosystem of interoperable tools.
Why Should You Care?
Whether you’re a developer, a business owner, or just someone interested in AI, MCP matters because it’s shaping how AI agents interact with the world:
- For developers: MCP means you can build tool integrations once and have them work across AI platforms. Less duplication, more reuse.
- For businesses: MCP-compatible tools make it easier to adopt AI assistants that can actually do things — not just generate text, but take actions in your systems.
- For website owners: As AI agents become a new kind of “visitor” to your site, supporting MCP means your content and services can be accessed more effectively by these agents. See our introduction to MCP for website owners for more on this.
- For everyone: MCP is a step toward AI agents that are genuinely useful — agents that can seamlessly interact with the tools and services we already use.
Getting Started with MCP
If you want to explore MCP further:
- Try it out — If you use Claude, you’re already using MCP. Many of Claude’s tool integrations are built on it.
- Build a server — The MCP specification is open, and there are SDKs available in Python and TypeScript to help you build your own MCP servers.
- Explore existing servers — There’s a growing library of community-built MCP servers for popular services and tools.
The Bottom Line
MCP is the emerging standard for how AI agents connect to the outside world. By providing a universal protocol for tool use, data access, and service interaction, it’s making AI systems more capable, more interoperable, and more useful.
As the ecosystem grows, MCP has the potential to become as fundamental to AI applications as HTTP is to the web. Understanding it now puts you ahead of the curve.