Shioaji MCP Server

Overview

The Shioaji MCP server is an end‑to‑end Model Context Protocol (MCP) implementation that bridges AI assistants with real‑world trading data and execution capabilities. It exposes a set of stock‑trading tools—such as market data retrieval, order placement, and portfolio monitoring—through the Shioaji API. By packaging these operations as MCP resources, developers can let AI agents query and act on live market information without writing custom integrations for each LLM provider.

Why It Matters

Traditional AI workflows often treat external data sources as black boxes, requiring manual API calls or custom adapters. Shioaji MCP turns the entire trading ecosystem into a first‑class MCP resource, enabling zero‑code AI assistants to ask for the latest price of a ticker, place an order, or fetch a portfolio summary—all within a single conversational prompt. This dramatically reduces the friction for fintech startups, quantitative researchers, and hobbyist traders who want to prototype AI‑powered trading strategies.

Key Features

• OpenAI‑compatible MCP host: The server can be run behind a lightweight OpenAI API proxy, allowing any LLM that speaks the Claude or OpenAI protocol to interact with it.
• Multi‑provider support: Switch between OpenAI and Ollama simply by setting an environment variable, giving developers flexibility in cost or latency trade‑offs.
• Certified MCP server: The Shioaji implementation has been reviewed and certified by the MCP Review board, ensuring it adheres to protocol best practices.
• Modular server configuration: The file lets you add or remove servers (e.g., Weather, Shioaji) and specify command arguments in a declarative way.

Real‑World Use Cases

• Automated portfolio management: An AI assistant can monitor risk metrics, rebalance holdings, and place orders based on predefined rules.
• Research prototyping: Quantitative analysts can quickly test new strategies by querying historical data and simulating trades through the MCP interface.
• Educational tools: Students learning algorithmic trading can interact with a live broker API via conversational AI, lowering the barrier to entry.

Integration Flow

1. Start the MCP host with your chosen LLM provider.
2. Configure server arguments in to launch the Shioaji MCP.
3. Send prompts to the host; the AI agent interprets commands and translates them into MCP calls.
4. Receive structured responses (e.g., order confirmation, market snapshot) that can be embedded directly in the conversation.

By turning trading operations into programmable MCP resources, Shioaji MCP empowers developers to build sophisticated AI assistants that can think and act in financial markets with minimal engineering overhead.