Scanpy MCP Server

The Scanpy MCP server bridges the gap between natural‑language interfaces and the full power of the Scanpy ecosystem for single‑cell RNA‑sequencing (scRNA‑Seq) analysis. It exposes Scanpy’s extensive data handling, preprocessing, analytical, and visualization capabilities as a set of callable tools that an AI assistant can invoke with plain English commands. By translating user intent into precise Scanpy function calls, the server eliminates the need for developers to write boilerplate code or learn the intricacies of Scanpy’s API, enabling rapid prototyping and deployment of scRNA‑Seq workflows within conversational agents.

At its core, the server offers four logical modules. The IO module lets assistants read and write scRNA‑Seq datasets using familiar terms such as “load my count matrix” or “save the processed data.” The preprocessing module covers all standard quality‑control steps—filtering cells and genes, normalizing counts, scaling data, selecting highly variable genes, performing PCA, and constructing neighborhood graphs—while allowing users to specify thresholds or parameters in natural language. The tool module provides access to downstream analyses like clustering, differential expression testing, and trajectory inference, again through intuitive prompts. Finally, the plotting module supports common visualizations (violin plots, heatmaps, dotplots) that can be requested by name and customized with descriptive arguments.

For developers building AI‑powered bioinformatics assistants, this server delivers a turnkey solution that can be integrated into any MCP‑compatible client—whether it’s a web UI like Cherry Studio, a plugin such as Cline, or an agent framework like Agno. The MCP protocol handles discovery, request routing, and result formatting, so the assistant’s natural‑language understanding layer can focus solely on interpreting user intent. The result is a smooth, end‑to‑end workflow where a researcher types “cluster the cells and plot a heatmap of the top markers,” and the assistant orchestrates all necessary Scanpy calls, returning visual results without any manual scripting.

Real‑world scenarios that benefit from this server include academic laboratories needing rapid exploratory analyses, clinical research pipelines requiring reproducible single‑cell workflows, and educational platforms that wish to provide interactive lessons on scRNA‑Seq without exposing students to complex code. The server’s modular design also allows developers to extend or customize individual tools, such as adding new preprocessing steps or integrating alternative plotting libraries, while maintaining a consistent natural‑language interface. In short, the Scanpy MCP server empowers AI assistants to deliver powerful single‑cell analysis capabilities in a user‑friendly, conversational manner.