MCP (Model Context Protocol) is an open standard that lets AI models connect to external tools and data sources through a single, consistent interface. Anthropic introduced it in late 2024 to replace the bespoke per-tool integrations developers used to build by hand — one shared protocol that works across any MCP-compatible AI host like Claude Desktop or Cursor.
TL;DR
- What it is: An open standard for AI-to-tool integration, introduced by Anthropic in late 2024.
- Why it exists: Before MCP, every AI tool needed custom integrations. MCP makes them portable across hosts.
- How it works: Hosts (Cursor, Claude Desktop) talk to Servers (filesystem, GitHub, database) via Clients using the MCP protocol.
- What servers expose: Tools (actions the AI can call), Resources (data the AI can read), Prompts (reusable templates).
- Why developers should care: Build an integration once, plug it into any MCP-compatible host. Less duplicated work, more leverage.
The problem MCP solves
AI assistants like Claude, GPT, and Gemini are powerful inside a conversation. But by default they're isolated. They can reason about text you give them, but they can't see your codebase, query your database, check your calendar, or interact with the tools you actually use. Every integration has to be custom-built — you wire up an API call here, a function there, and it's all bespoke plumbing that doesn't transfer between tools.
This is the problem Model Context Protocol (MCP) is designed to fix.
MCP is an open standard, introduced by Anthropic in late 2024, that defines a consistent way for AI models to connect to external tools and data sources. Instead of every AI tool reinventing its own integration layer, MCP gives developers a shared protocol — one way to build a connection that works across any MCP-compatible AI host.
Think of it like USB. Before USB, every device had its own connector. After USB, you plug anything into anything. MCP is trying to be that for AI tool integrations.
How it works
MCP has three main pieces:
Hosts are the AI applications the user interacts with — Cursor, Claude Desktop, or any app that's built MCP support in. The host manages connections to servers and mediates between the AI model and the outside world.
Servers are the integrations. An MCP server exposes a set of tools — things like "read a file," "query a database," "run a terminal command," or "fetch a web page." Servers can be local processes or remote services. They're relatively small, focused, and purpose-built.
Clients live inside the host and handle the communication between the host and each server using the MCP protocol.
When a user asks their AI assistant to do something that requires an external tool, the host checks what MCP servers are connected, picks the right tool, calls it, gets the result, and feeds it back to the model as context. The model never directly touches the external system — it just sees the results as part of its context window.
What MCP servers can expose
MCP servers can expose three types of things:
Tools are functions the AI can call — actions that do something. Run a shell command, create a file, send a message, query an API. These are the most common and most useful.
Resources are data the AI can read — files, database records, documents, anything that can be fetched and fed into context.
Prompts are reusable prompt templates the server makes available to the host — useful for standardizing how certain tasks get framed.
Most real-world MCP servers focus on tools. That's where the practical value is.
A concrete example
Say you're using Cursor to write code for a Godot game. Normally, Cursor can read files you paste in and suggest code, but it has no idea what's actually in your Godot project — what scenes exist, what nodes are in them, what the project structure looks like.
With a Godot MCP server running, Cursor can call tools like get_project_info, list_scenes, or get_node_tree and get real data back from your actual open project. The AI goes from working with whatever you manually paste in to working with live context from your development environment. That's a qualitatively different kind of assistance.
The same pattern applies everywhere: a filesystem MCP server lets the AI read and write files. A database MCP server lets it query your schema and run queries. A GitHub MCP server lets it read issues, PRs, and code. The AI stays the same — what changes is how much of your actual world it can see.
Why the standard matters
Before MCP, if you wanted Claude to talk to your database and Cursor to talk to the same database, you'd build two separate integrations. If a new AI tool came out that you wanted to try, you'd build a third.
With MCP, you build the server once. Any MCP-compatible host can connect to it. That's the compounding value of a shared standard — the integration work accumulates instead of being repeated.
It also means the ecosystem is growing fast. There are already MCP servers for filesystems, databases, web browsers, GitHub, Slack, Google Drive, and dozens of other tools. Most are open source. If a server exists for what you need, you configure it and connect it — no integration work required.
What this means if you're building with AI
A few practical implications:
If you're building AI-powered apps, MCP gives you a cleaner architecture for tool integrations. Instead of hardcoding API calls into your prompt pipeline, you can expose capabilities as MCP tools and let the model decide when and how to use them. It's more composable and easier to extend.
If you're using AI coding assistants, connecting MCP servers to your editor is one of the highest-leverage things you can do right now. Giving your AI assistant access to your actual project context — not just what you paste in — makes it meaningfully more useful.
If you're evaluating AI tools for your stack, MCP support is increasingly a signal worth paying attention to. Tools that support MCP plug into a growing ecosystem. Tools that don't are islands.
Getting started
The best place to start is Anthropic's MCP documentation. It covers the spec, has quickstart guides for building servers in Python and TypeScript, and links to the existing server ecosystem.
If you want to see it in action quickly, Claude Desktop supports MCP out of the box. Install it, configure a filesystem or fetch server in claude_desktop_config.json, and you'll have a working MCP setup in about ten minutes.
For Cursor users, MCP servers are configured in mcp.json in your project or user config directory. The Cursor docs cover the setup, and there are community-maintained lists of available servers worth browsing.
MCP is still early. The spec is evolving, the tooling is maturing, and not every AI host supports it yet. But the direction is clear — shared, composable tool integrations are better for everyone than bespoke one-off wiring. If you're building seriously with AI, it's worth understanding now.
FAQ
What is the difference between MCP hosts and servers?
A host is the AI application a user interacts with directly — Cursor, Claude Desktop, or any IDE that's added MCP support. A server is an integration that exposes a specific capability, like filesystem access, database queries, or the GitHub API. Hosts connect to one or more servers (via clients) so the AI model can use those capabilities as part of a conversation.
Is MCP only for Anthropic's models?
No. MCP is an open standard, and any AI host can implement support for it regardless of which underlying model it uses. Claude Desktop and Cursor were early adopters, but the protocol itself is model-agnostic and not tied to Claude.
Do I need to build my own MCP server?
Probably not. There are already open-source MCP servers for filesystems, GitHub, Slack, Google Drive, Postgres, web fetching, and dozens of other common tools. You only need to build a custom server when you have an internal system or workflow no existing server covers.
Are MCP servers safe to install?
Treat them like any other dependency. MCP servers run as local processes or remote services with whatever permissions you give them, so vet the code or the maintainer before connecting one to your editor — especially if the server can read files, hit your network, or execute shell commands.
How is MCP different from OpenAI function calling or tool use?
Function calling is a model-level feature — a single model deciding to call a function inside one app. MCP sits a layer above that: it standardizes how any host application discovers and connects to any tool integration. The same MCP server works with multiple hosts and models without rewriting the integration each time.
What languages can I use to build an MCP server?
The official SDKs cover Python and TypeScript today, and community SDKs exist for several other languages. Because MCP is just a protocol over standard transports, you can implement it in anything that can speak JSON-RPC over stdio or HTTP.
Top comments (0)