Biomodelling Servers

PyPI MCP Registry

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.

Publication

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

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.