cellrank-mcp

# cellrank-MCP Natural language interface for scRNA-Seq analysis with cellrank through MCP. ## 🪩 What can it do? - IO module like read and write scRNA-Seq data - 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 ## ❓ Who is this for? - Anyone who wants to do scRNA-Seq analysis natural language! - Agent developers who want to call cellrank's functions for their applications ## 🌐 Where to use it? You can use cellrank-mcp 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 cellrank-mcp ## 🏎️ Quickstart ### Install Install from PyPI ``` pip install cellrank-mcp ``` you can test it by running ``` cellrank-mcp run ``` #### run scnapy-server locally Refer to the following configuration in your MCP client: ``` "mcpServers": { "cellrank-mcp": { "command": "cellrank-mcp", "args": [ "run" ] } } ``` #### run scnapy-server remotely Refer to the following configuration in your MCP client: run it in your server ``` cellrank-mcp run --transport shttp --port 8000 ``` Then configure your MCP client, like this: ``` http://localhost:8000/mcp ``` ## 🤝 Contributing If you have any questions, welcome to submit an issue, or contact me(hsh-me@outlook.com). Contributions to the code are also welcome! ## Citing If you use cellRank-mcp in for your research, please consider citing following work: > 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