MCP Bio-Modelling Servers

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

All four 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 four console entry points:

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

ODE models with BioMASS

NeKo's export_biomass_handoff preserves the curated network's references and available mechanism metadata for BioMASS. The calling agent reads the literature, records evidence and assumptions, and authors Text2Model reactions. BioMASS supports standalone text too, along with graph rendering and bounded exploratory simulation. Calibration and sensitivity analysis are deferred.

For visualization, install mcp-biomodelling-servers[biomass-graph] and the Graphviz system runtime. See the BioMASS manual for an MCP client configuration, the 22 tools, graph interpretation limits, and a runnable example.

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
python -m BioMASS.server

Repository layout

MaBoSS/                     MaBoSS server, manual, and Registry manifest
NeKo/                       NeKo server, manual, and Registry manifest
PhysiCell/                  PhysiCell server, manual, and Registry manifest
BioMASS/                    ODE construction, visualization, and simulation server
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.

Releasing

The release.yml workflow runs the full CI and compatibility suites, builds and checks the wheel and source archive, publishes to PyPI, then publishes all four server manifests to the official MCP Registry. Both publishing steps use GitHub OIDC; no API token is needed.

Before the first automated release, configure a GitHub trusted publisher on the PyPI project's Publishing settings:

• Owner: marcorusc
• Repository: mcp-biomodelling-servers
• Workflow filename: release.yml
• Environment: pypi

Create the matching pypi environment in the repository's GitHub settings. See the PyPI trusted publishing guide and MCP Registry GitHub Actions guide.

For each release, synchronize the version in pyproject.toml, the source fallback in mcp_biomodelling_servers/__init__.py, and all four server.json files, including their pinned --from arguments. Build with python -m build, check with python -m twine check --strict dist/*, and run python scripts/check_release.py --tag v<version> using Python 3.12+. Start with a clean dist/ directory. Commit and push the reviewed changes before creating and pushing the matching v<version> tag to trigger publication.

If PyPI succeeds but MCP registration fails, manually dispatch the release workflow on the same release tag, with publish_pypi disabled. This retries registration without trying to upload the existing PyPI version again.

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.