What Is MCP? The Model Context Protocol Explained for Developers

What Is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard introduced by Anthropic in late 2024 that defines how AI language models communicate with external tools and data sources. It gives AI assistants like Claude, Cursor, and VS Code Copilot a standardized way to call APIs, read files, query databases, and interact with services — without any custom integration work on the client side.

Before MCP, every AI client had its own proprietary plugin system. A tool built for Claude Desktop wouldn't work in Cursor. A Cursor extension wouldn't work in VS Code. MCP solves this by defining a single transport protocol that any client can speak and any tool can implement.

How MCP Works

An MCP setup has three components:

MCP Client — the AI application (Claude Desktop, Claude Code, Cursor, VS Code Copilot, Windsurf). The client knows how to speak the MCP protocol and display available tools to the model.

MCP Server — a process that implements one or more tools. It receives tool call requests from the client, executes them (e.g., calls the Gmail API), and returns results. MCP servers communicate over stdio (standard input/output) or SSE (Server-Sent Events).

Tool definitions — each MCP server exposes a list of tools with a name, description, and JSON Schema for inputs. The AI model reads these definitions and decides when and how to call them.

A minimal MCP interaction looks like this:

User: "Search my Gmail for invoices from last month"

Claude reads tool list → sees gmail__search_emails tool
Claude calls: gmail__search_emails({ query: "invoice", after: "2026-03-01" })
MCP server executes → calls Gmail API → returns results
Claude reads results → summarizes for user

MCP Transport: stdio vs SSE

MCP supports two transport modes:

stdio — the client spawns the MCP server as a child process and communicates over stdin/stdout. Simple, no networking required. Most MCP servers use this mode.

SSE (Server-Sent Events) — the MCP server runs as an HTTP server and the client connects via SSE. Used for remote servers or persistent server processes. Syncore's daemon uses a Unix socket variant of this pattern internally.

Which AI Clients Support MCP?

As of 2026, MCP is supported by:

• Claude Desktop and Claude Code (Anthropic)
• Cursor
• VS Code (GitHub Copilot with MCP extension)
• Windsurf (Codeium)
• Zed editor
• Warp terminal
• Cline (VS Code extension)
• OpenAI Codex CLI

The list is growing rapidly as MCP adoption accelerates across the AI developer tools ecosystem.

How to Start Using MCP Tools Today

The fastest way to start using MCP tools is Syncore. One install configures all your AI clients with 50+ tools — Gmail, Slack, Notion, GitHub, Perplexity, Firecrawl, and more:

curl -fsSL https://syncorelabs.ai/install.sh | sh
syncore login
syncore setup

After syncore setup, every MCP-compatible AI client on your machine has access to all installed skills. No individual server configuration required.

Building Your Own MCP Server

If you want to build a custom MCP server, Anthropic maintains official SDKs for TypeScript and Python. A minimal TypeScript MCP server looks like:

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server({ name: "my-tool", version: "1.0.0" });

server.setRequestHandler("tools/list", async () => ({
  tools: [{
    name: "hello_world",
    description: "Returns a greeting",
    inputSchema: { type: "object", properties: { name: { type: "string" } } }
  }]
}));

server.setRequestHandler("tools/call", async (req) => ({
  content: [{ type: "text", text: `Hello, ${req.params.arguments.name}!` }]
}));

const transport = new StdioServerTransport();
await server.connect(transport);

For production MCP servers that need OAuth credentials, consider publishing to the Syncore skill registry — Syncore handles credential injection, token refresh, and distribution automatically.