mcp-biomodelling-servers 1.0.0

MCP servers for biological modelling: NeKo network analysis, MaBoSS Boolean dynamics, and PhysiCell multiscale simulation

MCP Bio‑Modelling Servers

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
MaBoSS	MaBoSS/	https://github.com/colomoto/pyMaBoSS
NeKo	NeKo/	https://github.com/sysbio-curie/Neko
PhysiCell (settings wrapper)	PhysiCell/	https://github.com/marcorusc/PhysiCell_Settings

All servers are Python processes speaking MCP over stdio.

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MCP Background

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).

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Repository Layout

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.

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Environment Assumption

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).

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Configure in VS Code (GitHub Copilot Chat / MCP)

1. Clone this repo somewhere stable (no spaces in path recommended).
2. Open VS Code.
3. Ensure the Copilot Chat (or other MCP‑capable) extension is installed and updated.
4. Press Ctrl + Shift + P and search for the command that opens the MCP configuration JSON (e.g. “MCP: Open Configuration” or locate mcp.json). On Linux it typically lives at: ~/.config/Code/User/mcp.json.
5. Add entries pointing to each server.py using the Conda environment’s Python.

Example (adapt paths to your system; based on the working setup):

{
  "servers": {
    "maboss": {
      "type": "stdio",
      "command": "/home/you/miniforge3/envs/mcp_modelling/bin/python",
      "args": [
        "/absolute/path/to/mcp-biomodelling-servers/MaBoSS/server.py"
      ],
      "env": {
        "PATH": "/home/you/miniforge3/envs/mcp_modelling/bin:${Path}",
        "CONDA_PREFIX": "/home/you/miniforge3/envs/mcp_modelling"
      }
    },
    "neko": {
      "type": "stdio",
      "command": "/home/you/miniforge3/envs/mcp_modelling/bin/python",
      "args": [
        "/absolute/path/to/mcp-biomodelling-servers/NeKo/server.py"
      ]
    },
    "physicell": {
      "type": "stdio",
      "command": "/home/you/miniforge3/envs/mcp_modelling/bin/python",
      "args": [
        "/absolute/path/to/mcp-biomodelling-servers/PhysiCell/server.py"
      ]
    }
  },
  "inputs": [
    {
      "id": "memory_file_path",
      "type": "promptString",
      "description": "Path to the memory storage file",
      "password": false
    }
  ]
}

Notes:

• Replace /home/you/... and /absolute/path/to/... with your actual directories.
• Keep all three servers referencing the same Conda interpreter to share installed libraries.
• Add further environment variables (e.g. data directories) per tool README if required.

After saving, reload / restart VS Code so the MCP client reconnects.

Activation / usage guidance in VS Code: https://code.visualstudio.com/docs/copilot/chat/mcp-servers

You should then see the servers’ tools listed in the Copilot Chat “/tools” (or similar) UI. Invoke them by name with required parameters.

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Adding Another Server

1. Create a new folder with server.py and a README describing the underlying modelling tool and dependencies.
2. Follow existing server structure for registering MCP tools.
3. Update your mcp.json with a new block (use the same Conda Python path).
4. Document any additional env vars in that folder README.

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License

Project is MIT (see existing LICENSE file). Underlying tools retain their own licenses — consult upstream repositories.

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Quick Reference

Action	What to Do
Get tool install steps	Open the tool’s subfolder README or upstream link
Ensure deps present	Install into your chosen Conda env (user‑defined)
Configure MCP	Edit ~/.config/Code/User/mcp.json as above
Reload servers	Reload VS Code window
Learn MCP	Spec: modelcontextprotocol.io; VS Code guide link above

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Happy modelling!