Text2sim MCP Server

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 via the Model Context Protocol (MCP), enabling powerful simulation capabilities within natural language environments like Claude Desktop.

How to use Text2sim MCP Server?

Install Python 3.12 or higher and the uv package manager. Clone the repository, then configure Claude Desktop by adding a block to claude_desktop_config.json that points uv to the mcp_server.py file. After setup, you describe a simulation in plain English (e.g., “Simulate a hospital with triage, diagnosis, and treatment”) and the LLM translates it into a configuration dictionary that the server runs as a SimPy simulation.

Key features of Text2sim MCP Server

• Create simulation models using plain English descriptions.
• Supports multiple domains: airport, healthcare, manufacturing, and more.
• Define custom entity attributes and behaviors.
• Configure steps with probability distributions (uniform, normal, exponential).
• Collect real-time metrics like wait times and throughput.
• Secure regex-based parsing – no eval() used.

Use cases of Text2sim MCP Server

• Simulate passenger processing at a small airport with check-in, security, and boarding.
• Model hospital triage, diagnosis, and treatment flows with staffing limits.
• Analyze manufacturing line throughput and bottleneck detection.
• Evaluate queuing systems for service counters or call centers.

FAQ from Text2sim MCP Server

What does Text2sim MCP Server do?

It takes a natural language description of a process, converts it into a SimPy simulation configuration, runs the simulation, and returns performance metrics like average wait times and completed counts.

How do I install and run the server?

You need Python 3.12+ and the uv package manager. After cloning the repo, add the server configuration to Claude Desktop’s claude_desktop_config.json as shown in the README.

What probability distributions are supported?

The distribution field accepts uniform(min, max), normal(mean, std) (or gauss), and exp(mean). All inputs are parsed safely with regex.

Is the server secure?

Yes. It uses regex-based parsing for distribution inputs, never eval(). Input validation and error handling prevent arbitrary code execution and information leaks.

What metrics does a simulation return?

For each process step, the server returns the average wait time and the number of entities that completed that step – for example, check_in_wait_time_avg and check_in_completed_count.