What analysis modules are available?
cellrank-MCP covers IO, preprocessing (filtering, QC, normalization, scaling, HVG, PCA, neighbors), tools (clustering, differential expression), and plotting (violin, heatmap, dotplot).
@scmcphub
基本信息
MCP server for trajectory inference using cellrank
工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
cellrank-MCP is a natural language interface for scRNA-Seq analysis that exposes cellrank functions through the Model Context Protocol. It is designed for researchers and agent developers who want to perform single-cell RNA sequencing workflows using conversational AI clients or agentic frameworks.
Install via pip (pip install cellrank-mcp), then run locally with the command cellrank-mcp run. To use it in an MCP client, configure the server with the full path to the binary and the run argument. For remote usage, start the server with cellrank-mcp run --transport shttp --port 8000 and set the client’s URL to http://localhost:8000/mcp.
cellrank-MCP covers IO, preprocessing (filtering, QC, normalization, scaling, HVG, PCA, neighbors), tools (clustering, differential expression), and plotting (violin, heatmap, dotplot).
Anyone who wants to perform scRNA-Seq analysis through natural language, as well as agent developers who need to call cellrank functions programmatically.
Install it from PyPI with `pip install cellrank-mcp`. Verify the installation with `cellrank-mcp run`, then configure your MCP client with the command and the `run` argument.
Yes. Start the server with `cellrank-mcp run --transport shttp --port 8000` and set your client’s URL to `http://localhost:8000/mcp`.
cellrank-MCP works with any MCP‑compatible client, including Cherry Studio (AI client), Cline (plugin), and Agno (agent framework).

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