Scanpy-MCP
About
A natural language interface for single-cell RNA sequencing (scRNA-Seq) analysis using the Scanpy library.
Details
- Author
- scmcphub
- Categories
- Developer Tools, Other
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Setup
Install Scanpy-MCP in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/scmcphub/scanpy-mcp
Follow the installation instructions in the repository README, then restart your MCP client.
Natural language interface for scRNA-Seq analysis with Scanpy through MCP.
- 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
- Anyone who wants to do scRNA-Seq analysis natural language!
- Agent developers who want to call scanpy's functions for their applications
You can use scanpy-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
scmcphub's complete documentation is available athttps://docs.scmcphub.org
A demo showing scRNA-Seq cell cluster analysis in a AI client Cherry Studio using natural language based on scanpy-mcp
https://github.com/user-attachments/assets/93a8fcd8-aa38-4875-a147-a5eeff22a559
Refer to the following configuration in your MCP client:
$ which scanpy /home/test/bin/scanpy-mcp
"mcpServers": { "scanpy-mcp": { "command": "//home/test/bin/scanpy-mcp", "args": [ "run" ] } }
Refer to the following configuration in your MCP client:
scanpy-mcp run --transport shttp --port 8000
Then configure your MCP client in local AI client, like this:
"mcpServers": { "scanpy-mcp": { "url": "http://localhost:8000/mcp" } }
If you have any questions, welcome to submit an issue, or contact me(hsh-me@outlook.com). Contributions to the code are also welcome!
If you use scanpy-mcp in for your research, please consider citing following work:
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
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