Provides a Jupyter backend for executing custom code and generating complete Jupyter notebooks containing scRNA-Seq analysis results, executable code, and visualizations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@SCMCPFilter the cells, perform clustering, and generate a UMAP plot."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
SCMCP
An MCP server for scRNA-Seq analysis with natural language!
🪩 What can it do?
IO module: Read and write scRNA-Seq data with natural language
Preprocessing module: Filtering, quality control, normalization, scaling, highly-variable genes, PCA, Neighbors,...
Tool module: Clustering, differential expression, etc.
Plotting module: Violin plots, heatmaps, dotplots
Cell-cell communication analysis
Pseudotime analysis
Enrichment analysis
❓ Who is this for?
Anyone who wants to do scRNA-Seq analysis using 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
📚 Documentation
scmcphub's complete documentation is available at https://docs.scmcphub.org
🎬 Demo
A demo showing scRNA-Seq cell cluster analysis in an 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:
You can test it by running:
🚀 Running Modes
SCMCP provides two distinct run modes to accommodate different user needs and preferences:
1. Tool Mode
In tool mode, SCMCP provides a curated set of predefined functions that the LLM can select and execute.
Advantages:
Stable: Predefined functions ensure consistent and reliable execution
Predictable: Known behavior and expected outputs
Safe: Controlled environment with validated operations
Disadvantages:
Limited flexibility: Restricted to available predefined functions; you need to define new tools when you need customization functions
Usage
Running in terminal:
Configure MCP client:
Examples:
2. Code Mode
In code mode, SCMCP provides a Jupyter backend that allows the LLM to generate and execute custom code. Additionally, it can generate complete Jupyter notebooks containing executable code, analysis results, and visualizations.
This mode is based on the project: https://github.com/huang-sh/abcoder
Advantages:
Highly flexible: Can create custom workflows and combine operations freely
Extensible: Supports any Python code and external libraries
Interactive: Real-time code execution and debugging capabilities
Disadvantages:
Less stable: Code generation may vary each time
Usage
Running in terminal:
Configure MCP client:
Example: https://youtu.be/3jtXIeapslI
📝 Mode Comparison
Feature | Tool Mode | Code Mode |
Execution Method | Predefined functions | Custom code generation |
Stability | High (consistent) | Lower (variable) |
Flexibility | Limited to available tools | Highly flexible |
Safety | Controlled environment | Full Python execution |
Use Case | Standard workflows | Custom analysis |
Learning Curve | Easy to use | Requires Python knowledge |
🌐 Remote Deployment
For both modes, you can also run SCMCP remotely:
Remote Setup
Start the server:
Configure your MCP client:
🤝 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 scmcp in your research, please consider citing the 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