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MCP server for biological network construction and analysis using pathway databases
About
MCP server for biological network construction and analysis using pathway databases
Security Report
Valid MCP server (2 strong, 1 medium validity signals). 3 known CVEs in dependencies Package registry verified. Imported from the Official MCP Registry. Trust signals: trusted author (3/3 approved).
5 files analyzed · 4 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-marcorusc-neko": {
"args": [
"mcp-biomodelling-servers",
"mcp-neko-server",
"mcp-biomodelling-servers"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
MCP Bio-Modelling Servers
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 bypiporuvx. - The Graphviz system runtime for NeKo history diagrams. The Python
graphvizpackage is not a replacement for the externaldotrenderer.
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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