by yusong652
MCP server connecting AI agents to ITASCA engines (PFC, FLAC, 3DEC, MPoint, MassFlow) — run geotechnical & geomechanics simulations through natural conversation
# Add to your Claude Code skills
git clone https://github.com/yusong652/itasca-mcpLast scanned: 6/28/2026
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itasca-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 engines (PFC, FLAC, 3DEC, MPoint, MassFlow) — run geotechnical & geomechanics simulations through natural conversation. It has 196 GitHub stars.
Yes. itasca-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/itasca-mcp" and add it to your Claude Code skills directory (see the Installation section above).
itasca-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 itasca-mcp against similar tools.
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
itasca>model new ;now, with LLM.
itasca-mcp connects AI agents to ITASCA's geomechanics simulation software — PFC, FLAC, 3DEC, MPoint, and MassFlow — through the Model Context Protocol. Browse documentation, run simulations, and execute code, all through natural conversation.
itasca>model solve ;LLM solves.

5 documentation tools — browse and search the selected engine's commands, Python API, and reference docs. No bridge required.
5 execution tools — interactive REPL, task submission, progress monitoring, interruption, and history. Requires bridge.
plot commands in the docs, then plot the contact force chains of the current model"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/itasca-mcp/main/docs/agentic/itasca-mcp-bootstrap.md
1. Register the MCP server with your agent.
Most agents register it with a single command:
# Claude Code
claude mcp add itasca-mcp -- uvx itasca-mcp
# Codex / Codex-cli
codex mcp add itasca-mcp -- uvx itasca-mcp
# Gemini CLI
gemini mcp add itasca-mcp uvx itasca-mcp
Or fill in the MCP config file manually:
{
"mcpServers": {
"itasca-mcp": {
"command": "uvx",
"args": ["itasca-mcp"]
}
}
}
2. Start the bridge from inside the ITASCA engine:
Download addon.py, then use either of these two flows inside the engine GUI (PFC, FLAC, 3DEC, ...):
Restart your AI agent and ask it to call itasca_execute_code to verify the connection.
Once first-time setup is done, each new engine session only needs the bridge re-started — run this in the engine'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.
software parameterversion parameterconsole.exeSee Troubleshooting in the bootstrap guide.
See Developer Guide: Install and Run from Source.
PRs and issues are welcome! See CONTRIBUTING.md for where changes land and how the documentation corpus is structured, and the Developer Guide for the source install.
MIT - see LICENSE.