Memory MCP Server
Persistent memory using a knowledge graph
MCP server for Boolean network simulation and attractor analysis using MaBoSS
{
"mcpServers": {
"io-github-marcorusc-maboss": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
MCP server for Boolean network simulation and attractor analysis using MaBoSS
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
License not specified.
Is it maintained?
Commit history unknown.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
This server is missing a description. Tools and install config are also missing.If you've used it, help the community.
Add informationNo known vulnerabilities.
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Persistent memory using a knowledge graph
Privacy-first. MCP is the protocol for tool access. We're the virtualization layer for context.
Pre-build reality check. Scans GitHub, HN, npm, PyPI, Product Hunt — returns 0-100 signal.
Hash-verified file editing MCP server with token efficiency hook. 11 tools for AI coding agents.
MCP Security Weekly
Get CVE alerts and security updates for io.github.marcorusc/MaBoSS and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
This repository centralizes Model Context Protocol (MCP) servers that wrap Python‑based mechanistic / systems biology modelling tools. Each subfolder contains a server.py entrypoint plus a README describing the specific tool interface.
Current servers (see their own READMEs & upstream docs):
| Tool | Folder | Upstream Documentation | MCP Registry |
|---|---|---|---|
| MaBoSS | MaBoSS/ | https://github.com/colomoto/pyMaBoSS | io.github.marcorusc/MaBoSS |
| NeKo | NeKo/ | https://github.com/sysbio-curie/Neko | io.github.marcorusc/NeKo |
| PhysiCell (settings wrapper) | PhysiCell/ | https://github.com/marcorusc/PhysiCell_Settings | io.github.marcorusc/PhysiCell |
All servers are Python processes speaking MCP over stdio.
pip install mcp-biomodelling-servers
Then run any server directly:
mcp-neko-server
mcp-maboss-server
mcp-physicell-server
uvx --from mcp-biomodelling-servers mcp-neko-server
uvx --from mcp-biomodelling-servers mcp-maboss-server
uvx --from mcp-biomodelling-servers mcp-physicell-server
Clone this repo and set up a Conda environment with all dependencies (see Environment Assumption below).
The Model Context Protocol standardizes how external tools expose tools and resources to AI assistants / IDEs. Spec & introduction: https://modelcontextprotocol.io/docs/getting-started/intro
Each server.py advertises modelling actions (e.g. run simulations, manage sessions) to any MCP‑aware client (e.g. VS Code with GitHub Copilot Chat MCP support).
MaBoSS/ # MaBoSS MCP server (Boolean / stochastic models)
NeKo/ # NeKo MCP server
PhysiCell/ # PhysiCell settings / sessions MCP server
README.md
Consult the README within each tool folder for: purpose, required Python packages, and any model/data file expectations. Installation instructions for the modelling tools themselves live there (or in the upstream project links above) — they are intentionally not duplicated here.
All tools are Python‑based. Create (and manage) a single Conda environment that contains the dependencies for MaBoSS, NeKo, and PhysiCell. The exact creation commands are up to you (not prescribed here). Once created, note the absolute path to its Python interpreter (e.g. /home/you/miniforge3/envs/mcp_modelling/bin/python).
Ctrl + Shift + P → "MCP: Open Configuration" (or edit ~/.config/Code/User/mcp.json directly).If you installed via pip or want to use uvx, no paths are needed:
{
"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"]
}
}
}
Use this if you need a custom Conda environment (e.g. for native MaBoSS bina