MCP for Brokerage: How Model Context Protocol Is Connecting AI to Trading.

The Model Context Protocol is the most consequential piece of AI infrastructure that almost no one in financial services is talking about. MCP is a standardized way for AI agents — Claude, Gemini, GPT — to invoke external tools with typed inputs and structured outputs. For trading, that means a single agent can check positions, submit orders, retrieve account data, and monitor fills as part of its reasoning loop.

Before MCP, connecting an AI agent to a brokerage meant building a bespoke integration for every API. Alpaca REST endpoints, Interactive Brokers TWS, Schwab's OAuth2 flow, TradeStation's streaming WebSocket — each required custom auth, custom error handling, and custom response parsing. The result was fragile plumbing that broke every time a vendor updated their API.

MCP collapses this. An MCP server wraps a brokerage API into a typed tool surface — function name, input schema, output schema — that any MCP-compatible agent can invoke natively. The agent does not need to know whether it is talking to Alpaca or Interactive Brokers. It calls `submit_order` with a typed payload and gets a typed response. The integration complexity lives in the server, not the agent.

The architecture looks like this: the AI agent runs a reasoning loop — read breaking news, analyze the options chain, check the restricted list, calculate position sizing. When it decides to act, it calls an MCP tool. The MCP server validates the request against pre-trade risk controls (position limits, notional limits, order rate limits), then forwards it to the brokerage API. Every tool call is logged with inputs, outputs, timestamps, and the full reasoning context. That is the audit trail.

We have been running this pattern in production since 2025. The Enso Trading Terminal connects to Alpaca, Public.com, and crypto exchanges via MCP servers that we built and maintain. The agent reads a news article about an FDA approval, checks the options chain for the relevant ticker, verifies risk limits, calculates sizing, and submits — all in one agentic loop. The entire flow is logged and reviewable.

The practical implication for financial institutions: MCP is the new SaaS integration layer for finance. Every brokerage, market data provider, and compliance system should be evaluating how to publish an MCP surface. The firms that ship MCP servers first will own the distribution layer for the next generation of AI-native trading infrastructure. We build these servers. They are usually two- to six-week engagements.