CellRank-MCP
MCP server that exposes the CellRank cell-fate workflow as natural-language tools — build kernels, fit a GPCCA estimator, predict initial/terminal states, and compute fate probabilities.
| Type | MCP server |
| Supplier | scmcphub |
| Availability | GA — v0.4.0 (2025-06-27) |
| Pricing | Free / OSS — BSD-3-Clause (per the scmcphub ecosystem; per-repo LICENSE file not separately confirmed) |
| Capabilities | Read/Write — runs analysis on a loaded AnnData object and writes plots |
How to install
Install the package first (required before any registration snippet below works):
pip install cellrank-mcp
- Claude Code — stdio (recommended; Claude Code launches the process itself, so do not run
cellrank-mcp runseparately):claude mcp add --transport stdio cellrank -- cellrank-mcp run - Claude Desktop — add to
claude_desktop_config.json:{ "mcpServers": { "cellrank": { "command": "cellrank-mcp", "args": ["run"] } } } - HTTP transport (long-lived service — keep it running in its own terminal; not needed for the stdio setups above):
cellrank-mcp run --transport shttp --port 8000
To verify the install before registering, run cellrank-mcp run once and Ctrl-C after it boots — for stdio the host launches it for you.
What it does
create_kernel/compute_transition_matrix— build a kernel and its cell-to-cell transition matrixcreate_and_fit_gpcca— fit the GPCCA estimatorpredict_terminal_states/predict_initial_states— identify macrostatescompute_fate_probabilities— per-cell fate probabilities toward terminal stateskernel_projection/circular_projection— trajectory visualizations
Primary use cases: Cell-fate and trajectory modeling, terminal/initial state discovery, fate-probability mapping for developmental single-cell data.
Notes
Built on CellRank. Part of the scmcphub single-cell ecosystem alongside scanpy-mcp, liana-mcp, and decoupler-mcp — typically run together on a shared AnnData object. RNA-velocity kernels require velocity estimates (e.g., from scVelo) precomputed on the object.
Sources
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