What Is MCP?
Model Context Protocol, explained for developers who keep hearing the term but haven't dug in yet.
The one-sentence version
MCP (Model Context Protocol) is a standard that lets AI agents use external tools — make real HTTP requests, query databases, read files, run code, and interact with APIs — instead of just generating text.
Why it exists
Large language models are good at reasoning about code, but they can't do anything. Ask Claude or GPT to check if your API is returning a 200 — it'll give you a confident answer, but it didn't actually make the request. It guessed.
MCP fixes this. It's a protocol (created by Anthropic, now supported by most major AI platforms) that lets an AI agent call external tools during a conversation. The agent decides when it needs a tool, calls it through MCP, gets real results back, and uses those results to continue working.
↓
AI Agent → MCP Server → http_request("https://api.example.com")
↓
Real response: { status: 503, body: "Service Unavailable" }
↓
Agent: "No — it's returning 503. Here's what that means..."
How it works (the 30-second version)
An MCP server is a small program that exposes a set of tools. Each tool has a name, a description, and input parameters — like a function signature. The AI agent reads the tool list, decides which tool to call based on the conversation, sends a request to the MCP server, and gets structured results back.
The protocol uses JSON-RPC over stdio (for local servers) or HTTP with server-sent events (for remote servers). But you don't need to think about any of that to use one.
What can MCP servers do?
Anything a program can do. Common categories:
Network operations — HTTP requests, DNS lookups, SSL certificate checks, port scanning. The agent can actually test your infrastructure instead of guessing.
Data access — query databases, read spreadsheets, search internal documents. The agent works with your real data, not hallucinated examples.
External APIs — GitHub, Slack, Jira, AWS, Stripe. The agent can create issues, send messages, deploy code, check billing.
File operations — read, write, search, transform files on your machine. The agent can refactor code across your entire codebase.
Computation — cryptographic hashing, regex execution, data transformation. Things where LLMs approximate but get wrong.
Who supports it?
MCP was created by Anthropic and is now supported by Claude Desktop, Claude Code, Cursor, Windsurf, Cline, Continue, Amazon Q Developer, and a growing list of other AI tools. The ecosystem has over 11,000 servers and millions of monthly SDK downloads.
How do I start using MCP servers?
If you're using Claude Desktop, Cursor, or another MCP-compatible tool, you add a server by putting a few lines of JSON in your settings file. That's it — the agent automatically discovers the tools and starts using them when relevant.
How do I build my own MCP server?
An MCP server is a Node.js (or Python) program that uses the MCP SDK. A basic server with one tool is about 30 lines of code. A production server with 15+ tools, error handling, and documentation is a few hundred lines.