Wrapping an analysis library usually means inventing a new vocabulary on top of it; here the modules stay where they were, so someone who knows Scanpy can predict what is available and someone who does not can be walked through a standard workflow without learning the API first. The remote transport is the part worth planning around: single-cell data is large, and running the server where the data lives rather than shipping the data to your laptop is what makes this practical.
A natural-language front end to Scanpy, the standard Python toolkit for single-cell RNA sequencing analysis. It mirrors Scanpy's own module structure, so the steps an analysis takes are the steps you already know — they are just reachable by asking rather than by writing the call.
- The IO module: reading and writing single-cell datasets
- The preprocessing module — filtering, quality control, normalization, scaling, highly-variable gene selection, PCA and neighbour graph construction
- The tools module, covering clustering and differential expression
- The plotting module: violin plots, heatmaps and dot plots
- The same surface whether you drive it from an AI client, an editor plugin or an agent framework — the README names Cherry Studio, Cline and Agno as working hosts
No account and no key. Install `scanpy-mcp` from PyPI, then either point your client at the installed executable with its run subcommand for a local stdio setup, or start it on a server with a streamable-HTTP transport and a port and connect the client to that URL. Documentation for the project lives at the maintainers' docs site.
One command — pip install scanpy-mcp