How the Model Context Protocol connects AI agents to trading infrastructure.
The Model Context Protocol (MCP) is an open standard that defines how AI models interact with external tools and data sources. Developed by Anthropic, MCP provides a structured interface for LLMs to call functions in external systems — retrieving data, placing orders, managing accounts — without screen scraping or brittle automation scripts.
TraderEvolution's MCP Server exposes 31 tools that give AI agents structured access to the full trading infrastructure. It is the first production MCP implementation for multi-asset trading, built on top of the Client API.
MCP solves a fundamental problem in AI integration: how do you give an AI model access to real-world systems in a safe, structured, and reliable way?
Before MCP, connecting AI to trading required custom integration work — parsing unstructured API responses, handling authentication, managing state. MCP standardises this with a tool-based interface: each capability is exposed as a named tool with defined inputs and outputs. The AI model invokes tools through structured JSON calls, and the MCP Server handles execution and response formatting.
Example: An AI agent wants to check the current price of AAPL. Instead of parsing a REST API response, it calls get_quote(symbol="AAPL") and receives a structured response with bid, ask, last price, volume, and timestamp. The agent can reason about this data and decide whether to place an order by calling place_order(symbol="AAPL", side="buy", quantity=100, type="market").
TraderEvolution's MCP Server organises its 31 tools into functional groups:
Market data tools: Get real-time quotes, market depth, historical price bars, option chains, and instrument details. These tools provide the data AI agents need to analyse markets and make decisions.
Order management tools: Place market, limit, stop, and complex orders. Modify pending orders. Cancel orders. Retrieve order status and execution reports. All orders go through the same risk management checks as orders from any other front-end.
Account tools: Retrieve positions, balances, P&L, and trade history. Access account details and portfolio composition. These tools enable AI agents to monitor and report on portfolio performance.
Instrument tools: Search for instruments, retrieve contract specifications, and discover available markets. Essential for agents that need to navigate a multi-asset instrument universe.
AI trading assistants: Build conversational interfaces where clients interact with an AI that can retrieve market data, explain portfolio positions, and execute orders on their behalf. The neo-broker use case — a modern investing app powered by AI.
Autonomous trading agents: Deploy AI agents that monitor markets, execute strategies, and manage risk autonomously. Prop firms can use AI agents to manage funded accounts, enforce risk rules, and optimise execution.
Back-office automation: AI agents can perform account monitoring, compliance checks, position reconciliation, and exception handling — using the same tools that power client-facing trading.