Enables autonomous multi-study scientific paper analysis, consensus ratio calculation, citation credibility verification, and multi-hop research queries through the Model Context Protocol.
Enables autonomous auditing of scientific papers for methodology flaws, identifying selection bias and p-hacking, while computing consensus ratios and verifying citation credibility through MCP-compliant tooling.
Enables decomposition of complex research queries into multi-hop sub-queries and synthesis DAGs, with scientific consensus analysis, citation credibility verification, and deterministic JSON outputs for MCP-compliant clients.
A comprehensive Model Context Protocol server that provides AI assistants with direct access to Semantic Scholar's academic database, enabling advanced paper discovery, citation analysis, author research, and AI-powered recommendations.