scmcp 0.2.2

An MCP server for scRNA-Seq analysis with natural language!

SCMCP

An MCP server for scRNA-Seq analysis with natural language!

🪩 What can it do?

• IO module like read and write scRNA-Seq data with natural language
• Preprocessing module,like filtering, quality control, normalization, scaling, highly-variable genes, PCA, Neighbors,...
• Tool module, like clustering, differential expression etc.
• Plotting module, like violin, heatmap, dotplot
• cell-cell communication analysis
• Pseudotime analysis
• enrichment analysis

❓ Who is this for?

• Anyone who wants to do scRNA-Seq analysis natural language!
• Agent developers who want to call scanpy's functions for their applications

🌐 Where to use it?

You can use scmcp in most AI clients, plugins, or agent frameworks that support the MCP:

• AI clients, like Cherry Studio
• Plugins, like Cline
• Agent frameworks, like Agno

🎬 Demo

A demo showing scRNA-Seq cell cluster analysis in a AI client Cherry Studio using natural language based on scmcp

https://github.com/user-attachments/assets/93a8fcd8-aa38-4875-a147-a5eeff22a559

🏎️ Quickstart

Install

Install from PyPI

pip install scmcp

you can test it by running

scmcp run

run scnapy-server locally

Refer to the following configuration in your MCP client:

"mcpServers": {
  "scmcp": {
    "command": "scmcp",
    "args": [
      "run"
    ]
  }
}

run scnapy-server remotely

Refer to the following configuration in your MCP client:

run it in your server

scmcp run --transport shttp --port 8000

Then configure your MCP client, like this:

http://localhost:8000/smcp

🤝 Contributing

If you have any questions, welcome to submit an issue, or contact me([e-mail]). Contributions to the code are also welcome!

Citing

If you use scmcp in for your research, please consider citing following works:

Wolf, F., Angerer, P. & Theis, F. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15 (2018). https://doi.org/10.1186/s13059-017-1382-0

Dimitrov D., Schäfer P.S.L, Farr E., Rodriguez Mier P., Lobentanzer S., Badia-i-Mompel P., Dugourd A., Tanevski J., Ramirez Flores R.O. and Saez-Rodriguez J. LIANA+ provides an all-in-one framework for cell–cell communication inference. Nat Cell Biol (2024). https://doi.org/10.1038/s41556-024-01469-w

Badia-i-Mompel P., Vélez Santiago J., Braunger J., Geiss C., Dimitrov D., Müller-Dott S., Taus P., Dugourd A., Holland C.H., Ramirez Flores R.O. and Saez-Rodriguez J. 2022. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances. https://doi.org/10.1093/bioadv/vbac016

Weiler, P., Lange, M., Klein, M. et al. CellRank 2: unified fate mapping in multiview single-cell data. Nat Methods 21, 1196–1205 (2024). https://doi.org/10.1038/s41592-024-02303-9