by yusong652
MCP server connecting AI agents to ITASCA PFC — run DEM simulations through natural conversation
# Add to your Claude Code skills
git clone https://github.com/yusong652/pfc-mcpLast scanned: 6/22/2026
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}pfc-mcp is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by yusong652. MCP server connecting AI agents to ITASCA PFC — run DEM simulations through natural conversation. It has 100 GitHub stars.
Yes. pfc-mcp passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/yusong652/pfc-mcp" and add it to your Claude Code skills directory (see the Installation section above).
pfc-mcp is primarily written in Python. It is open-source under yusong652 on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh pfc-mcp against similar tools.
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pfc3d>model new ;now, with LLM.
pfc-mcp connects AI agents to ITASCA PFC — Itasca's discrete element method (DEM) code — through the Model Context Protocol. Browse documentation, run simulations, and execute code, all through natural conversation.
pfc3d>model solve ;LLM solves.

5 documentation tools — browse and search PFC commands, Python API, and reference docs. No bridge required.
5 execution tools — interactive REPL, task submission, progress monitoring, interruption, and history. Requires bridge.
uvx)Copy this to your AI agent and let it self-configure:
Fetch and follow this bootstrap guide end-to-end:
https://raw.githubusercontent.com/yusong652/pfc-mcp/main/docs/agentic/pfc-mcp-bootstrap.md
1. Register the MCP server in your client config:
{
"mcpServers": {
"pfc-mcp": {
"command": "uvx",
"args": ["pfc-mcp"]
}
}
}
2. Start the bridge from inside PFC:
Download addon.py, then use either of these two flows inside PFC:
Restart your AI agent (Claude Code, Codex CLI, Gemini CLI, etc.) and ask it to call pfc_execute_code to verify the connection.
Once first-time setup is done, each new PFC session only needs the bridge re-started — run this in PFC's IPython console and you're back online:
import itasca_mcp_bridge
itasca_mcp_bridge.start()
start() checks PyPI for a newer bridge release and self-upgrades before starting. The MCP client config persists.
version parameterSee Troubleshooting in the bootstrap guide.
See Developer Guide: Install and Run from Source.
PRs and issues are welcome! See the Developer Guide to get started.
MIT - see LICENSE.