What Is MCP? The Model Context Protocol Explained for Traders

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 talk to external tools and data sources. It gives assistants like Claude, Cursor, and ChatGPT a standardized way to call APIs, read files, and query services — without custom integration work on the client side.

For a trader, that's the difference between an AI that *guesses* where a stock is trading from stale training data and one that *pulls* the live quote, computes the RSI, and checks the next earnings date before answering.

How MCP Works

An MCP setup has three components:

MCP Client — the AI application (Claude Desktop, Claude Code, Cursor, ChatGPT). It speaks the MCP protocol and shows available tools to the model.

MCP Server — a process that implements tools. It receives tool-call requests from the client, executes them (e.g., calls Polygon for bars), and returns results over stdio or SSE. TradeLoop is an MCP server for market data.

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

A minimal MCP interaction for a trader looks like this:

User: "What's TSLA's 14-day RSI and is it overbought?"

Claude reads tool list → sees a market-data indicator tool
Claude calls: get_indicators({ symbol: "TSLA", indicators: ["rsi14"] })
MCP server executes → fetches bars from Polygon → computes RSI → returns 71.3
Claude reads result → "RSI is 71.3, modestly overbought."

MCP Transport: stdio vs SSE

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

SSE (Server-Sent Events) — the server runs as an HTTP server and the client connects via SSE. Used for remote or persistent servers. TradeLoop's local daemon uses a loopback variant of this pattern internally, bound to 127.0.0.1.

Which AI Clients Support MCP?

As of 2026, MCP is supported by:

• Claude Desktop and Claude Code (Anthropic)
• Cursor
• ChatGPT (desktop, via connectors)
• VS Code (GitHub Copilot with MCP extension)
• Windsurf, Zed, Warp, Cline

The list is growing fast as MCP adoption accelerates across AI developer and trading tools.

How to Start Using Market Tools Today

The fastest way to give your AI agent live market data is TradeLoop. One install configures all your AI clients with tools backed by Polygon, Finnhub, CoinGecko, FRED, and SEC EDGAR:

curl -fsSL https://tradeloop.top/install.sh | sh
tradeloop login
tradeloop setup

After tradeloop setup, every MCP-compatible client on your machine can pull quotes, precomputed indicators, earnings calendars, and filings. No per-client configuration required.

Building Your Own MCP Server

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

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

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

server.setRequestHandler("tools/list", async () => ({
  tools: [{
    name: "get_quote",
    description: "Returns the latest price for a ticker",
    inputSchema: { type: "object", properties: { symbol: { type: "string" } } }
  }]
}));

server.setRequestHandler("tools/call", async (req) => ({
  content: [{ type: "text", text: `Latest price for ${req.params.arguments.symbol}` }]
}));

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

For production tools that need provider keys and OAuth refresh, consider publishing to the TradeLoop skill registry — TradeLoop handles credential injection, token refresh, and distribution automatically.