What is Cellrank Mcp

cellrank-mcp is an MCP server designed for trajectory inference in single-cell RNA sequencing (scRNA-Seq) analysis using the CellRank framework. It provides a natural language interface for users to interact with scRNA-Seq data.

Use cases

Use cases for cellrank-mcp include performing scRNA-Seq analysis using natural language commands, enabling agent developers to integrate CellRank functions into their applications, and facilitating data visualization and analysis in AI environments.

How to use

To use cellrank-mcp, install it via PyPI with ‘pip install cellrank-mcp’. You can run it locally or remotely by configuring your MCP client accordingly. For local use, execute ‘cellrank-mcp run’, and for remote access, run ‘cellrank-mcp run --transport shttp --port 8000’ and set up your MCP client to connect to the server.

Key features

Key features of cellrank-mcp include an IO module for reading and writing scRNA-Seq data, preprocessing capabilities like filtering and normalization, tools for clustering and differential expression analysis, and various plotting options such as violin plots and heatmaps.

Where to use

cellrank-mcp can be used in various AI clients, plugins, or agent frameworks that support MCP, including Cherry Studio, Cline, and Agno.

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

📚 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