FactorHub MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| FACTORHUB_API_KEY | No | Your FactorHub API key (optional, defaults to free trial quota). Get it from https://factorhub.cn/api-keys |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| set_api_keyA | 设置你的 FactorHub API Key。注册获取：https://factorhub.cn/api-keys。设置后本次会话将使用你的专属额度。 |
| list_factorsA | 获取因子列表。支持按分类和关键词搜索。返回因子代码、名称、分类、年化收益、夏普比率等。 |
| get_factor_scoresB | 获取单个因子的详细评分指标：年化收益、夏普比率、最大回撤、波动率、Alpha、Beta、IC均值等。 |
| get_factor_navC | 获取因子净值曲线数据，用于分析因子历史表现趋势。 |
| get_market_dailyB | 获取个股日线行情数据（OHLCV + 涨跌幅）。ts_code 如 000001.SZ、600519.SH。 |
| get_index_dailyC | 获取指数日线行情。常用：000001.SH(上证)、399001.SZ(深证)、000300.SH(沪深300)、000905.SH(中证500)。 |
| get_valuationC | 获取股票估值指标：PE、PB、PS、股息率、总市值、流通市值、换手率等。 |
| get_stock_infoB | 获取单只股票基本信息：名称、行业、上市日期、市场板块等。 |
| get_stock_listC | 按条件筛选股票列表。可按交易所(SSE/SZSE)、行业筛选。 |
| get_trade_datesB | 获取交易日历，查询指定时间段内的交易日列表。 |
| run_backtestB | 执行量化策略回测。内置策略：limit_up_first_board(涨停首板)、low_valuation(低估值)、momentum(动量)、mean_reversion(均值回归)。返回年化收益、夏普比率、最大回撤等。 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 11 tools
Each tool targets a distinct aspect of financial data analysis: factor performance vs scores, index vs stock data, stock info vs list, valuation, backtesting, and configuration. No two tools serve the same purpose.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., get_factor_nav, list_factors, run_backtest). The conventions are uniform across all 11 tools.
With 11 tools, the server covers core operations for factor analysis, stock data, index data, backtesting, and configuration. The count is well-scoped without being overwhelming or insufficient.
The tool set covers essential CRUD-like operations for factor data, stocks, indices, and backtesting. Minor gaps exist, such as lack of bulk data retrieval or more customizable backtest parameters, but the core workflows are well-supported.