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MaBoSS Boolean Network Simulator MCP Server

io.github.marcorusc/MaBoSS

Simulate and analyze stochastic Boolean network models with MaBoSS for systems biology research.

What is the MaBoSS Boolean Network Simulator MCP server?

The MaBoSS MCP server provides tools to configure, simulate, and analyze stochastic Boolean network models using the pyMaBoSS library. It enables researchers to study biological system dynamics, compute attractors, and explore network behavior through mechanistic modeling.

MaBoSS is a stochastic Boolean network simulator designed for systems biology. It allows you to build Boolean models of biological networks, run simulations with stochastic transitions, and analyze attractor states—useful for understanding cell fate decisions, signaling pathways, and other discrete biological processes.

How to install MaBoSS Boolean Network Simulator

Copy-paste configuration for popular MCP clients.

transport: stdio
Config generated by PluginBench — verify against the source before use.
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "MaBoSS": {
      "command": "uvx",
      "args": [
        "mcp-biomodelling-servers",
        "--from",
        "mcp-biomodelling-servers",
        "mcp-maboss-server"
      ]
    }
  }
}

Tools & capabilities

Tools this server exposes to the agent.

  • Configure Boolean models — Set up and configure stochastic Boolean network models
  • Simulate networks — Run stochastic simulations of Boolean networks
  • Analyze attractors — Identify and analyze attractor states in Boolean network dynamics

Use cases

  • Model cell signaling pathways and predict steady-state behaviors
  • Simulate biological network dynamics under different conditions
  • Identify stable states (attractors) in gene regulatory networks
  • Explore how network perturbations affect system outcomes
  • Integrate Boolean models into multi-scale biological simulations

MaBoSS Boolean Network Simulator MCP server FAQ

What is the MaBoSS MCP server?

MaBoSS is an MCP server that wraps the pyMaBoSS library to enable stochastic Boolean network simulation and attractor analysis. It lets you configure, run, and analyze discrete dynamical systems models of biological networks.

Is MaBoSS free?

Yes, MaBoSS is open-source and distributed under the MIT license as part of the mcp-biomodelling-servers package.

How do I install MaBoSS in Cursor or Claude?

Install via pip with `python -m pip install mcp-biomodelling-servers`, then configure your MCP client to use the `mcp-maboss-server` entry point via stdio.

What Python versions does it support?

MaBoSS requires Python 3.10–3.14 and the MCP Python SDK 2.x.

Do I need authentication or API keys?

No, MaBoSS is a local modeling tool that does not require authentication or external API keys.

Can I use MaBoSS with other modeling servers?

Yes, MaBoSS is part of a package with NeKo and PhysiCell servers; you can run multiple servers together and pass artifacts between them.

README (reference)

Source of truth, from the repository.

MCP Bio-Modelling Servers

<!-- mcp-name: io.github.marcorusc/NeKo --> <!-- mcp-name: io.github.marcorusc/MaBoSS --> <!-- mcp-name: io.github.marcorusc/PhysiCell -->

PyPI MCP Registry

This package provides three stateful Model Context Protocol servers for mechanistic and systems-biology modelling:

ServerModelling roleUpstream projectMCP Registry name
MaBoSSConfigure, simulate, and analyze stochastic Boolean modelspyMaBoSSio.github.marcorusc/MaBoSS
NeKoBuild and analyze signalling networks from interaction databasesNeKoio.github.marcorusc/NeKo
PhysiCellBuild, inspect, and export PhysiCell and PhysiBoSS configuration filesPhysiCell-settingsio.github.marcorusc/PhysiCell

All three servers use MCP over stdio and are distributed together as mcp-biomodelling-servers.

Publication

For more details, please check the related article:

"Intelligent tool orchestration for rapid mechanistic model prototyping: MCP servers as AI-biology interfaces"<br> Marco Ruscone, Miguel Vazquez & Alfonso Valencia, npj Systems Biology and Applications (2026)<br> https://doi.org/10.1038/s41540-026-00767-3

Requirements

  • Python 3.10–3.14.
  • MCP Python SDK 2.x, installed automatically with this package.
  • The modelling-package dependencies declared in pyproject.toml, installed automatically by pip or uvx.
  • The Graphviz system runtime for NeKo history diagrams. The Python graphviz package is not a replacement for the external dot renderer.

Check whether Graphviz is available with:

dot -V

If this command is missing, install Graphviz using your operating system or environment package manager. See the Graphviz installation guide for platform-specific instructions.

Installation

Install with pip

python -m pip install mcp-biomodelling-servers

The installation provides three console entry points:

mcp-neko-server
mcp-maboss-server
mcp-physicell-server

Run in an isolated environment with uvx

uvx --from mcp-biomodelling-servers mcp-neko-server
uvx --from mcp-biomodelling-servers mcp-maboss-server
uvx --from mcp-biomodelling-servers mcp-physicell-server

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.

Configure an MCP client

The following example uses uvx and works with clients that accept the common mcp.json stdio configuration:

{
  "servers": {
    "neko": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "mcp-biomodelling-servers",
        "mcp-neko-server"
      ]
    },
    "maboss": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "mcp-biomodelling-servers",
        "mcp-maboss-server"
      ]
    },
    "physicell": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "mcp-biomodelling-servers",
        "mcp-physicell-server"
      ]
    }
  }
}

If the package is already installed in the client environment, each entry can instead use its console script directly:

{
  "servers": {
    "neko": {
      "type": "stdio",
      "command": "mcp-neko-server"
    },
    "maboss": {
      "type": "stdio",
      "command": "mcp-maboss-server"
    },
    "physicell": {
      "type": "stdio",
      "command": "mcp-physicell-server"
    }
  }
}

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.

Sessions, artifacts, and errors

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.

Run from source

Clone the repository and install it with its development dependencies:

git clone https://github.com/marcorusc/mcp-biomodelling-servers.git
cd mcp-biomodelling-servers
python -m pip install ".[dev]"

You can then run the same console entry points or invoke a server module directly with the selected Python interpreter:

python MaBoSS/server.py
python NeKo/server.py
python PhysiCell/server.py

Repository layout

MaBoSS/                     MaBoSS server, manual, and Registry manifest
NeKo/                       NeKo server, manual, and Registry manifest
PhysiCell/                  PhysiCell server, manual, and Registry manifest
mcp_biomodelling_servers/   Installed package namespace and entry points
tests/                      Protocol, runtime, concurrency, and package tests

The server-specific READMEs describe the modelling workflows and exposed tool families in more detail.

MCP SDK and protocol compatibility

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.

License

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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