What is Text2sim Mcp Server

Text2Sim MCP Server is a discrete-event simulation engine that generates and executes flexible SimPy-based models from natural language descriptions. It integrates with Large Language Models (LLMs) using the Model Context Protocol (MCP), enabling powerful simulation capabilities.

Use cases

Use cases include simulating airport traffic management, healthcare patient flow, manufacturing assembly lines, and any scenario where understanding complex systems through simulation is beneficial.

How to use

To use Text2Sim MCP Server, clone the repository from GitHub, install the required dependencies, and configure it with Claude Desktop by editing the configuration file to integrate the simulation models.

Key features

Key features include LLM integration for model creation using plain English, multi-domain support for various industries, configurable entity attributes and behaviors, stochastic process logic for probabilistic modeling, real-time metrics collection, and a secure implementation using regex-based parsing.

Where to use

Text2Sim MCP Server can be used in various domains such as airport operations, healthcare, manufacturing, and any field that requires discrete-event simulation for process optimization and analysis.

Text2Sim MCP Server

Multi-paradigm Simulation Engine for LLM Integration

Text2Sim MCP Server is a simulation engine that supports multiple modeling paradigms, including Discrete-Event Simulation (DES) and System Dynamics (SD). It integrates with LLMs using the Model Context Protocol (MCP), enabling powerful simulation capabilities within natural language environments like Claude Desktop.

🚀 Features

• Large Language Model (LLM) Integration
  Create simulation models using plain English descriptions to LLMs.

• Multi-Paradigm Support

  • Discrete-Event Simulation (DES) using SimPy for process-oriented models
  • System Dynamics (SD) using PySD for feedback-driven continuous models

• Multi-Domain Support
  Build simulations for domains such as airport operations, healthcare, manufacturing, supply chains, and more.

• Flexible Model Configuration

  • DES: Configurable entities with stochastic process logic
  • SD: Stock-and-flow models with feedback loops and time-based equations

• Real-Time Metrics

  • DES: Performance indicators such as wait times and throughput
  • SD: Time series data for stocks, flows, and auxiliaries

• Secure Implementation
  Uses regex-based parsing (not eval()) for processing distribution inputs and safe model execution.

🔧 Installation

Prerequisites

• Python 3.12 or higher
• uv package manager

Install uv

On macOS and Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

On Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Learn more: astral-sh/uv

🛠️ Usage

Clone the repository

git clone https://github.com/IamCatoBot/text2sim-MCP-server.git

Integration with Claude Desktop

1. Open:

Claude > Settings > Developer > Edit Config > claude_desktop_config.json

2. Add the following block:

{
  "mcpServers": {
    "Text2Sim MCP Server": {
      "command": "uv",
      "args": [
        "--directory",
        "PATH_TO_TEXT2SIM_MCP_SERVER",
        "run",
        "mcp_server.py"
      ],
      "env": {}
    }
  }
}

Note: If the uv command is not found, run which uv (Unix) or Get-Command uv (PowerShell) and use the full path in the "command" field.

📚 API Reference

Overview

The MCP server provides tools for both Discrete-Event Simulation and System Dynamics modeling:

• Discrete-Event Simulation: Process-oriented modeling with SimPy
• System Dynamics: Stock-and-flow modeling with PySD

When using a Large Language Model (e.g. Claude) client, natural language prompts are translated into appropriate configurations via the Model Context Protocol (MCP).

🏗️ Architecture

Text2Sim is structured into modular components:

• MCP Server – Handles natural language requests via MCP.
• Discrete-Event Simulation (DES) Module
  • Simulation Model – Core SimPy engine that executes process flows.
  • Entity Class – Represents units flowing through the system.
  • Process Steps – Encapsulate logic for each process stage.
  • Metrics Collector – Gathers statistics like wait times and throughput.
  • Secure Distribution Parser – Parses probability distributions safely.
• System Dynamics (SD) Module
  • Model Registry – Manages available SD models.
  • PySD Integration – Runs stock-and-flow models using PySD.
  • Simulation Controls – Time steps, durations, and parameter adjustments.
  • Results Formatter – Structures time series data for output.

For detailed documentation of each module, see:

• DES Module Documentation
• SD Module Documentation

🔐 Security Considerations

• No eval() usage
  Regex-based parsing prevents arbitrary code execution.

• Input Validation
  Distribution types, parameters, and model configurations are validated before execution.

• Robust Error Handling
  Errors are reported cleanly without leaking internal state.

🤝 Contributing

Pull requests are welcome! Please fork the repo and submit a PR. Suggestions, bug reports, and feature ideas are always appreciated.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.