StockNewsAI

StockNewsAI is an AI tool. StockNews.AI turns company headlines into structured signal cards with AI summaries, sentiment, importance and source links. It offers a watchlist, filtered alerts, and a REST plus MCP API for research tools and ChatGPT.

StockNews.AI is a market news service that condenses company coverage into a single feed. Instead of scanning dozens of headlines, you get compact signal cards that explain what changed, why it may matter for the business, and where the story came from. The platform pulls from established outlets such as Reuters, CNBC, Benzinga and Business Insider, and also folds in corporate events like earnings releases, buybacks and acquisitions. Each card carries a sentiment rating, an importance score, a confidence figure and a link to the underlying reporting. In practice, the tool works as a research starting point. A watchlist keeps the companies you own or follow in one place, and the Signal Desk surfaces the latest events for those tickers with a short AI view on potential short term impact. Users can set filters on importance and sentiment and receive email or push alerts, which suits investors who want to catch up in a few minutes rather than read every article. The analysis is explicitly framed as orientation, not advice, and the original source link is always one click away for verification. There is a developer layer as well. Paid plans include a REST API and an MCP server that can be plugged into ChatGPT or other compatible AI clients, so builders can retrieve ticker specific news, sentiment ratings and importance scores for research agents, dashboards or backtesting notebooks. The typical user is a self directed investor, analyst or builder who wants structured news context without scraping and cleaning articles by hand. Compared with general financial news portals, StockNews.AI trades breadth of editorial for brevity and machine readable signals. It does not execute trades, does not provide personalized financial advice, and its AI assessments can be wrong, so it works best alongside primary research rather than as a replacement for it.