Headline Vibes Analysis MCP Server

Headline Vibes Analysis MCP Server

Headline Vibes is a Model Context Protocol server designed to give AI assistants instant, data‑driven insight into the emotional tone of news headlines across the United States. By tapping into a curated set of major U.S. outlets—AP, Reuters, CNN, Fox News, NBC, ABC, WSJ, Washington Post, USA Today, Bloomberg, Business Insider and Time—the server aggregates up to a hundred headlines per request and produces a single sentiment score on a 0‑10 scale, where zero represents the most negative tone and ten the most positive. This concise metric allows developers to quickly gauge public mood, detect shifts in media framing or simply enrich conversational agents with up‑to‑date contextual awareness.

The server solves a common pain point for AI developers: accessing reliable, sentiment‑annotated news data without building and maintaining a custom scraper or NLP pipeline. Instead of parsing raw headlines themselves, developers can call the single tool and receive a structured response that includes not only the overall score but also a synopsis, headline count, source distribution and up to five sample headlines. The inclusion of source distribution gives agents the ability to mention which outlets are contributing most to a particular sentiment trend, adding credibility and nuance to their replies.

Key capabilities include natural‑language date parsing (“yesterday”, “last Friday”, “two weeks ago”) alongside explicit YYYY‑MM‑DD input, enabling flexible scheduling of queries. The server guarantees an even spread across the available news sources, preventing any single outlet from dominating the sentiment picture. Error handling is robust: clear messages are returned for invalid dates, unparseable queries, empty headline sets or external API failures, allowing client code to gracefully fall back or prompt the user for clarification.

In practice, Headline Vibes can power a variety of real‑world scenarios. A financial assistant could report market sentiment trends before trading decisions; a customer support bot might gauge public reaction to a brand’s press release; or a journalism research tool could track how coverage of a political event evolves over days. By integrating this MCP server into existing AI workflows, developers can enrich conversations with timely, objective sentiment data without the overhead of building and maintaining their own news‑analysis infrastructure.