Enables auditing scientific papers for methodological biases such as selection bias and p-hacking, and assessing citation credibility and research consensus.
Enables automated scientific paper analysis, citation credibility verification, consensus ratio calculation, and multi-hop research queries through the Model Context Protocol, integrating with MCP-compliant clients.
Enables autonomous multi-study scientific paper analysis, consensus ratio calculation, citation credibility verification, and multi-hop research queries through the Model Context Protocol.
Enables AI-native statistical analysis and reproducible research workflows through MCP, including natural language planning, protocol-based analysis, Python/R cross-validation, and publication-ready figure generation.