MCP server for Boolean network simulation and attractor analysis using MaBoSS
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π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
This package provides three stateful Model Context Protocol servers for mechanistic and systems-biology modelling:
| Server | Modelling role | Upstream project | MCP Registry name |
|---|---|---|---|
| MaBoSS | Configure, simulate, and analyze stochastic Boolean models | pyMaBoSS | io.github.marcorusc/MaBoSS |
| NeKo | Build and analyze signalling networks from interaction databases | NeKo | io.github.marcorusc/NeKo |
| PhysiCell | Build, inspect, and export PhysiCell and PhysiBoSS configuration files | PhysiCell-settings | io.github.marcorusc/PhysiCell |
All three servers use MCP over stdio and are distributed together as
mcp-biomodelling-servers.
For more details, please check the related article:
"Intelligent tool orchestration for rapid mechanistic model prototyping: MCP servers as AI-biology interfaces"
Marco Ruscone, Miguel Vazquez & Alfonso Valencia, npj Systems Biology and Applications (2026)
https://doi.org/10.1038/s41540-026-00767-3
pyproject.toml, installed
automatically by pip or uvx.graphviz
package is not a replacement for the external dot renderer.Check whether Graphviz is available with:
If this command is missing, install Graphviz using your operating system or environment package manager. See the Graphviz installation guide for platform-specific instructions.
The installation provides three console entry points:
Conda is optional. It remains useful when you want one explicitly managed environment for local development or additional native scientific software, but it is not required for the packaged entry points.
The following example uses uvx and works with clients that accept the common
mcp.json stdio configuration:
If the package is already installed in the client environment, each entry can instead use its console script directly:
Refer to your MCP client's documentation for its configuration-file location and reload procedure. For Visual Studio Code, see Use MCP servers in VS Code.
Each server can maintain multiple isolated modelling sessions. Tools that create or load a model return a session identifier; pass that identifier to subsequent operations when more than one session is active.
Generated models, configuration files, plots, and other outputs are kept in session-scoped artifact directories. Artifact-listing tools return the paths needed to inspect or hand files to another modelling server.
Under MCP SDK 2.x, failures to execute a tool are returned as tool errors so the client and model can distinguish them from successful scientific results. Validation tools may still return a successful result describing an invalid model or configuration when validity itself is the requested result.
Clone the repository and install it with its development dependencies:
You can then run the same console entry points or invoke a server module directly with the selected Python interpreter:
The server-specific READMEs describe the modelling workflows and exposed tool families in more detail.
The package uses the stable MCP Python SDK 2.x API. The SDK negotiates the
appropriate MCP protocol revision with the connected client; the protocol
revision is independent of the MCP Registry schema used by each server.json.
The package metadata declares the project under the MIT license. The wrapped modelling packages retain their own licenses; consult their upstream projects for details.
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