u/rangeva

[Webz.io](http://Webz.io) News Search API combines **natural-language search** with filters that go beyond the basics.

Instead of building complex Boolean queries, you can describe what you’re looking for in plain language, then narrow the results by **sentiment** and **category**, in addition to source, country, language, and date.

That makes searches like these much easier:

* Negative news about a company * Cybersecurity incidents in the technology sector * Positive financial news around a specific market * Political or business news related to a particular event

The API searches across 300K+ news sources and returns relevant articles and matching content chunks.

Useful for RAG, AI agents, media monitoring, financial research, risk intelligence, and competitive analysis.

You also get **$5 in free credit every month**, so it’s easy to test and build with before committing.

[https://docs.webz.io/docs/webz/news-search-api\](https://docs.webz.io/docs/webz/news-search-api)

We built a news search API for RAG — looking for feedback

We have been working on a new News Search API at Webz.io, specifically with RAG and AI agents in mind.

The basic idea is simple as you know is using natural-language query like:

>

The API searches Webz.io’s news data and returns the most relevant articles and matching content chunks.

You can also filter by date, country, language, source, sentiment, and category.

For RAG, the content chunks are probably the part I’m most interested in. You can feed the relevant section into your context rather than passing an entire article through the pipeline.

We also built an MCP server, so agents can search the news directly without having to build a separate API integration.

Docs: https://docs.webz.io/docs/webz/news-search

I’d be interested in feedback from people building RAG systems: when retrieving news, what matters more to you: retrieval quality, freshness, source coverage, or getting smaller relevant chunks back?