Investigative journalism with automated source aggregation
A news organization deployed an AI research assistant that monitors and searches 200+ news sites, government databases, and public records, enabling journalists to discover story connections and fact-check claims in seconds instead of days. MCPner.com's crawler updated indices in real-time as breaking news emerged.
The Problem
Investigative journalists spent 60-70% of their time manually searching through news archives, government sites, and public records to verify facts and discover story angles. Tracking competitor coverage required reviewing dozens of news sites daily. Fact-checking political claims involved hunting through historical statements and voting records across multiple databases.


How MCPFlow Solved It
They configured MCPner.com's crawler to monitor 200+ sources: major news sites, local news outlets, government databases (FEC filings, congressional records), and public records repositories. The crawler ran on a schedule, detecting and indexing new content within minutes of publication. Journalists connected to the MCP server via Claude Desktop, enabling natural language queries like 'Find all mentions of Senator X voting on climate bills in the last 5 years' or 'What did Company Y's CEO say about layoffs before the merger announcement?'
MCP Stack Used:
Measurable Outcomes
Research time for investigative pieces dropped 64%, allowing journalists to publish stories 3-4 days faster than competitors. The team broke 5 major stories in Q1 by discovering connections between public records and news reports that manual search had missed. Fact-checking accuracy improved with AI surfacing contradictory historical statements and voting records instantly.
Key Metrics:

Claude now does in 30 seconds what used to take our team 3 days. We're breaking stories faster because AI handles the grunt work of source aggregation.
Rachel Kim
Investigative Editor, Metro News Network
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