This MCP server, developed by Fred Em, provides sentiment analysis for news headlines from major US publications. Built with TypeScript and leveraging the Model Context Protocol SDK, it offers tools for analyzing headline sentiment on specific dates or using natural language queries. The implementation focuses on providing a normalized sentiment score, source distribution information, and sample headlines. By connecting AI models with current news sentiment data, this server enables sophisticated analysis of media trends and public opinion. It's particularly useful for applications and AI assistants that require insights into news sentiment, supporting use cases such as market analysis, public relations monitoring, and social media trend forecasting.
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Highlight AIDaily sentiment snapshot for a single day. Arguments: { 'input': string }. Accepts natural language or 'YYYY-MM-DD'. Returns investor/general scores, synopses, distributions, sample headlines, and diagnostics.
Monthly aggregation between two months. Arguments: { 'startMonth': 'YYYY-MM', 'endMonth': 'YYYY-MM' }. Outputs per-month political sentiments, headline counts, and token/sampling diagnostics.
