Add pfc-mcp: MCP server for ITASCA PFC discrete element simulation

Adds pfc-mcp to the Data Science Tools category. pfc-mcp is an MCP
server that provides AI agents with documentation browsing, script
execution, plot capture, and task management for ITASCA PFC (Particle
Flow Code), a leading discrete element method (DEM) simulation tool
used in geotechnical engineering research.

Published on PyPI: https://pypi.org/project/pfc-mcp/
This commit is contained in:
yusong652
2026-03-14 06:54:26 +09:00
parent 166d4f43b0
commit 26cedfd784

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@@ -915,6 +915,7 @@ Integrations and tools designed to simplify data exploration, analysis and enhan
- [kdqed/zaturn](https://github.com/kdqed/zaturn) 🐍 🏠 🪟 🐧 🍎 - Link multiple data sources (SQL, CSV, Parquet, etc.) and ask AI to analyze the data for insights and visualizations.
- [mckinsey/vizro-mcp](https://github.com/mckinsey/vizro/tree/main/vizro-mcp) 🎖️ 🐍 🏠 - Tools and templates to create validated and maintainable data charts and dashboards.
- [optuna/optuna-mcp](https://github.com/optuna/optuna-mcp) 🎖️ 🐍 🏠 🐧 🍎 - Official MCP server enabling seamless orchestration of hyperparameter search and other optimization tasks with [Optuna](https://optuna.org/).
- [yusong652/pfc-mcp](https://github.com/yusong652/pfc-mcp) 🐍 🏠 🪟 - MCP server for [ITASCA PFC](https://www.itascacg.com/software/pfc) discrete element simulation — browse documentation, execute scripts, capture plots, and manage long-running tasks via a WebSocket bridge to the PFC GUI.
- [phisanti/MCPR](https://github.com/phisanti/MCPR) 🏠 🍎 🪟 🐧 - Model Context Protocol for R: enables AI agents to participate in interactive live R sessions.
- [phuongrealmax/code-guardian](https://github.com/phuongrealmax/code-guardian) 📇 🏠 - AI-powered code refactor engine with 80+ MCP tools for code analysis, hotspot detection, complexity metrics, persistent memory, and automated refactoring plans.
- [pramod/kaggle](https://github.com/KrishnaPramodParupudi/kaggle-mcp-server) 🐍 - This Kaggle MCP Server makes Kaggle more accessible by letting you browse competitions, leaderboards, models, datasets, and kernels directly within MCP, streamlining discovery for data scientists and developers.