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PhysiCell Configuration Builder MCP Server

io.github.marcorusc/PhysiCell

Build, inspect, and export PhysiCell and PhysiBoSS configuration files with AI assistance.

What is the PhysiCell Configuration Builder MCP server?

The PhysiCell Configuration Builder MCP server enables building, inspecting, and exporting PhysiCell and PhysiBoSS configuration files through an AI interface. It is part of a suite of mechanistic and systems-biology modelling servers that integrate with Claude and other MCP clients to streamline computational biology workflows.

This server provides tools for constructing and managing PhysiCell agent-based modelling configurations and PhysiBoSS Boolean model settings. It maintains isolated modelling sessions, generates configuration files, and integrates with other bio-modelling servers to support rapid prototyping of mechanistic models in systems biology.

How to install PhysiCell Configuration Builder

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": {
    "PhysiCell": {
      "command": "uvx",
      "args": [
        "mcp-biomodelling-servers",
        "--from",
        "mcp-biomodelling-servers",
        "mcp-physicell-server"
      ]
    }
  }
}

Use cases

  • Build and configure PhysiCell agent-based models for multicellular simulations
  • Export PhysiCell configuration files ready for simulation
  • Inspect and validate PhysiCell and PhysiBoSS configuration parameters
  • Integrate PhysiCell model setup with Boolean network models via PhysiBoSS
  • Manage multiple independent modelling sessions for parallel model development

PhysiCell Configuration Builder MCP server FAQ

What is the PhysiCell Configuration Builder MCP server?

It is an MCP server that provides tools for building, inspecting, and exporting configuration files for PhysiCell (agent-based modelling framework) and PhysiBoSS (coupling with Boolean models). It integrates with Claude and other MCP clients to support computational biology workflows.

Is this server free to use?

Yes. The server is open-source under the MIT license and is distributed via PyPI as part of the mcp-biomodelling-servers package.

How do I install it in Cursor or Claude?

Install via pip with `python -m pip install mcp-biomodelling-servers`, then configure your MCP client (Cursor, Claude Desktop, or VS Code) to run the `mcp-physicell-server` console entry point via stdio. Alternatively, use `uvx --from mcp-biomodelling-servers mcp-physicell-server` for isolated execution.

Does it require authentication?

No authentication is required. The server operates locally and does not connect to external services.

What Python versions are supported?

Python 3.10 through 3.14 are supported. The server requires the MCP Python SDK 2.x, which is installed automatically.

Can I run multiple modelling sessions at once?

Yes. Each server maintains multiple isolated modelling sessions. Tools return session identifiers that you pass to subsequent operations when managing multiple sessions.

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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