Infercnv-MCP Overview

Infercnv-MCP provides a natural‑language interface for inferring copy number variations (CNVs) from single‑cell RNA sequencing data using the infercnvpy library. By exposing a Model Context Protocol (MCP) server, it allows AI assistants to request CNV analysis directly from raw scRNA‑seq matrices, eliminating the need for manual scripting or command‑line interaction. This makes advanced genomic analyses accessible to researchers who prefer conversational workflows and to developers building AI‑powered bioinformatics tools.

The server is organized into modular components that mirror the typical infercnvpy pipeline. An IO module handles reading and writing of scRNA‑seq data, including automatic loading of gene position annotations. A preprocessing module computes neighborhood graphs and prepares the data for CNV inference, ensuring that downstream steps receive properly normalized inputs. The tool module performs the core CNV inference, generating per‑cell copy number profiles and associated scores that indicate confidence or severity. Finally, a plotting module offers visual outputs such as chromosome heatmaps and dimensionality‑reduction plots (UMAP, t‑SNE), enabling quick inspection of inferred CNVs across cell populations.

For developers and researchers, this server translates complex bioinformatics workflows into simple natural‑language requests. An AI assistant can ask to “infer CNVs from my scRNA‑seq dataset” or “show a chromosome heatmap for cluster 3”, and the MCP server will execute the entire pipeline, return quantitative results, and provide visualizations—all without the user writing code. This is particularly valuable in collaborative environments where domain experts need to iterate rapidly on data analyses or when integrating CNV inference into larger AI agents that orchestrate multi‑step biological investigations.

Key use cases include cancer genomics studies where CNV patterns distinguish malignant subclones, developmental biology projects that track chromosomal aberrations across differentiation trajectories, and any scenario requiring rapid CNV assessment from single‑cell data. By integrating with popular MCP‑compatible clients such as Cherry Studio, Cline plugins, or Agno agent frameworks, the server can be embedded in existing AI workflows, enabling seamless data flow from raw sequencing files to actionable insights.

Unique advantages of Infercnv-MCP lie in its end‑to‑end automation, rich visual output, and strict adherence to the MCP specification. Developers benefit from a well‑defined API that handles data ingestion, preprocessing, inference, and plotting in a single call, while researchers gain reproducible, interpretable CNV results delivered through natural language. This combination of accessibility and technical depth makes Infercnv-MCP a powerful addition to any AI‑augmented bioinformatics toolkit.