What this replaces is the boilerplate around an analysis you already know how to do: loading positions, computing neighbours, running inference, plotting. That makes it useful for exploring a dataset conversationally and for agent code that needs CNV inference as one step among several. It is a wrapper, so the underlying method, its assumptions and its limits are infercnvpy's, and worth reading there before you trust a heatmap.
An MCP server wrapping infercnvpy, the Python package for inferring copy number variation from scRNA-seq data. It covers the whole path in one place: reading and writing the data and loading gene positions, computing neighbours and preparing the object, running the CNV inference and scoring, and plotting the result.
- Data read in and written back out, with gene position information loaded — the IO module
- Neighbour computation and the preparation steps the inference expects — the preprocessing module
- CNV inference over the prepared object, plus a CNV score — the tool module
- Chromosome heatmaps and UMAP or t-SNE embeddings of the result — the plotting module
No account and no key — it runs against data on your own machine. Install with `pip install infercnv-mcp`; `infercnv-mcp run` starts it over stdio, and a client entry points at the absolute path of the installed binary with the `run` argument. To reach it from another machine, `infercnv-mcp run --transport shttp --port 8000` serves it at `http://localhost:8000/mcp`. The pyproject name is `infercnv-mcp`. Full documentation for the scmcphub family is at docs.scmcphub.org.
One command — pip install infercnv-mcp