About MCP Server for Shioaji
MCP Server for Shioaji exposes Taiwan-market trading data from the Shioaji API as tools for AI assistants. It supports price lookup, historical K-bars, and stock scans.
Key features
- 01Current stock prices
- 02Historical K-bar data
- 03Stock lists and rankings
Use cases
- 01Check current prices for selected symbols
- 02Retrieve candlestick data for a date range
- 03Rank stocks by volume or price movement
MCP support
It exposes Shioaji API functions as MCP tools for AI assistants, including price queries, historical data, and stock scans.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Server for Shioaji
A Model Context Protocol (MCP) server that provides AI assistants with access to Shioaji trading API for the Taiwanese financial market.
Overview
This server implements the MCP protocol to expose Shioaji API functionality as tools that can be used by AI assistants. It allows AI models to:
- Retrieve current stock prices
- Fetch historical data
- List available stocks
- And more...
Installation
Prerequisites
- Python 3.10 or higher
- uv (fast Python package manager)
Using uv
uv sync
Configuration
Before running the server, you need to configure your Shioaji API credentials. There are two ways to do this:
Environment Variables
Set the following environment variables:
export SHIOAJI_API_KEY="your_api_key"
export SHIOAJI_SECRET_KEY="your_secret_key"
Using .env File
Create a .env file in the root directory with the following content:
SHIOAJI_API_KEY=your_api_key
SHIOAJI_SECRET_KEY=your_secret_key
Running the Server
Start the server with:
uv run mcp-server-shioaji
The server will start on http://0.0.0.0:8000 by default.
Available Tools
The server exposes the following tools via MCP:
get_stock_price
Get the current price of a stock by its symbol.
{
"tool": "get_stock_price",
"params": {
"symbols": "TW.2330,TW.2317"
}
}
Response will include price information for the requested stocks, including open, high, low, close prices, volume, and other trading data.
get_kbars
Fetch K-Bar (candlestick) data for a stock within a date range.
{
"tool": "get_kbars",
"params": {
"symbol": "TW.2330",
"start_date": "2023-12-01",
"end_date": "2023-12-15"
}
}
If start_date is not provided, it defaults to today. If end_date is not provided, it defaults to the same as start_date.
scan_stocks
Scan stocks based on various ranking criteria.
{
"tool": "scan_stocks",
"params": {
"scanner_type": "VolumeRank",
"ascending": false,
"limit": 10
}
}
Supported scanner types:
VolumeRank- Ranking by trading volumeAmountRank- Ranking by trading amountTickCountRank- Ranking by number of transactionsChangePercentRank- Ranking by percentage changeChangePriceRank- Ranking by price changeDayRangeRank- Ranking by daily range
Default limit is 20, and results are sorted in descending order by default (set ascending to true for ascending order).
Development
Project Structure
mcp-server-shioaji/
├── src/
│ └── mcp_server_shioaji/
│ ├── __init__.py # Package entry point
│ └── server.py # MCP server implementation
├── pyproject.toml # Project metadata and dependencies
└── README.md # This file
Adding New Tools
To add new Shioaji functionality, modify server.py and add new tool definitions using the @mcp.tool decorator.
License
MIT
Acknowledgements
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.