Enables decomposition of complex, multi-faceted research prompts into executable query plans and synthesis DAGs, with support for scientific evidence analysis and consensus calculation.
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 provenance-first scholarly retrieval, paper ingestion, and reproducible research workflows by searching academic and developer sources, extracting source-located facts and claims, and preserving evidence and provider uncertainty for MCP clients.
An MCP server that leverages graph structures to perform sophisticated scientific reasoning through an 8-stage processing pipeline, enabling AI systems to handle complex scientific queries with dynamic confidence scoring.