Enables deep probabilistic analysis of single-cell omics data using scvi-tools through natural language. Supports SCVI for scRNA-seq analysis, SCANVI for cell type annotation, TOTALVI for multi-modal RNA/protein data, and PEAKVI for scATAC-seq analysis.
Provides a natural language interface for scRNA-Seq analysis using the Scanpy library, supporting operations such as data preprocessing, clustering, and visualization. It enables AI agents and clients to perform complex single-cell transcriptomics workflows through the Model Context Protocol.
Enables natural language interface for single-cell RNA-Seq analysis using Liana. Supports reading/writing scRNA-Seq data, cell-cell communication analysis, and visualization through circle plots and dotplots.
Provides a natural language interface for single-cell RNA-Seq analysis using the decoupleR framework. It enables users to perform biological pathway inference, data clustering, and visualization through MCP-compatible AI clients.
Enables natural language interaction for scRNA-Seq analysis including preprocessing, clustering, and visualization using the CellRank library. It allows users and agents to perform complex genomic data tasks through standard MCP clients and frameworks.
Enables automated cell type annotation in scRNA-seq analysis using CellTypist models, with tools for listing, downloading, training, and annotating cell types via natural language.
