About this skill

Problem

Financial data queries are often scattered across market quotes, financials, funds, bonds, macro indicators, and news announcements. Engineers building analysis scripts must decide which market and asset class to query, then assemble the right parameters. This skill aggregates iFinD MCP, westock-data, and westock-tool into a single call surface for tasks such as intelligent stock selection, quote summaries, financial metrics, news search, and real-time market data.

How It Works

The core call is call(server_type, tool_name, params). server_type selects the data domain, such as stock, fund, bond, global_stock, index, edb, or news; tool_name selects the concrete tool; and params usually contains a natural-language query, for example “electronics companies with market cap above 10 billion” or “CSI 300 return over the last 10 trading days.” For high-frequency real-time quotes, the documentation recommends structured parameters instead, including symbols, indicators, data_mode, and interval; data_mode must be explicitly set to real_time or highfreq.

The skill also routes requests by use case: default stock data can use stock; chip distribution, dragon-tiger lists, margin trading, northbound flows, and ETF data are better suited to westock-data; condition, strategy, and label-based selection are better suited to westock-tool. The implementation prefers Node.js, with Python as an alternative.

Boundaries

ifind concurrency is plan-limited: 2 req/s for free, 5 req/s for personal, and 10 req/s for enterprise.
Intraday high-frequency data is valid only during trading days and does not support historical data.
westock-data features such as chips, dragon-tiger lists, block trades, and margin trading cover only certain A-share markets.
westock-tool does not support the Beijing Stock Exchange, and strategy/label selection is limited to A-shares.
HK/US queries may return HKD or USD; do not assume CNY notation.

Screenshots

Use Cases

Quant researcher screens A-share electronics stocks with market cap above 10 billion and ROE criteria.
Fund analyst retrieves recent fund returns, holder flows, and equity portfolio exposure for comparison.
Macro strategist searches new-energy indicators, then pulls monthly PV cell production data for models.
Event-driven trader retrieves IPO, buyback, dividend notices, and hot events for real-time monitoring.

Best For

Quant or strategy engineer: needs to turn natural-language stock filters into executable data queries.
Financial data analyst: needs to retrieve A-share, fund, bond, index, and macro data across sources.
Investment research team member: needs to search news, notices, hot events, and company events.
Data or ML engineer: needs to integrate real-time quotes and conditional selection into analysis pipelines.