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.
Enables scientific literature research through multi-agent search, analysis, and semantic memory, exposing 9 MCP tools for querying, storing, and retrieving research findings.
MCP server for AI-assisted research: paper ingestion, semantic search, citation graph traversal, cross-domain knowledge synthesis, and workflow automation.
Enables AI assistants to run reproducible bioinformatics pipelines over MCP, with verifiable provenance via checksums and Workflow Run RO-Crate metadata.