About cellrank-MCP
cellrank-MCP is an MCP server for single-cell RNA-seq analysis, intended for researchers and agent developers using CellRank functions. It provides a natural-language interface to data handling, preprocessing, analysis, and plotting capabilities.
Key features
- 01Read and write scRNA-Seq data
- 02Filter, quality control, normalize, and scale
- 03Highly variable genes, PCA, and neighbors
- 04Clustering and differential expression
- 05Violin, heatmap, and dot plots
Use cases
- 01Run scRNA-Seq analysis through natural language
- 02Preprocess single-cell RNA-seq data
- 03Perform clustering or differential expression
- 04Call CellRank functions from agent applications
MCP support
The MCP server exposes CellRank data handling, preprocessing, analysis, and plotting functions as tools for MCP clients. It can run locally or remotely over shttp transport.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEcellrank-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
📚 Documentation
scmcphub's complete documentation is available at https://docs.scmcphub.org
🎬 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 cellrank-mcp locally
Refer to the following configuration in your MCP client:
check path
$ which cellrank
/home/test/bin/cellrank-mcp
"mcpServers": {
"cellrank-mcp": {
"command": "/home/test/bin/cellrank-mcp",
"args": [
"run"
]
}
}
run cellrank-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 in local AI client, like this:
"mcpServers": {
"cellrank-mcp": {
"url": "http://localhost:8000/mcp"
}
}
🤝 Contributing
If you have any questions, welcome to submit an issue, or contact me([email protected]). 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
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.