Text2Sim MCP Server – Overview

The Text2Sim MCP Server bridges the gap between conversational AI and formal simulation modeling. By exposing a Model Context Protocol interface, it lets large language models (LLMs) such as Claude describe, validate, and execute simulation scenarios using plain natural‑language prompts. The server parses the LLM’s output into a JSON‑structured configuration, runs it through either SimPy (Discrete‑Event Simulation) or PySD (System Dynamics), and returns a rich analytics payload that the LLM can interpret, summarize, or iterate upon.

This capability solves a key pain point for developers who want to prototype complex systems—logistics networks, supply chains, or epidemic spread models—without writing boilerplate simulation code. Instead of manually translating requirements into Python scripts, a developer can simply describe the system in conversational form, let the LLM generate the model, and receive immediate feedback on performance metrics such as queue lengths, utilization rates, or stock trajectories. The server’s built‑in schema validation ensures that only well‑formed configurations reach the simulation engine, reducing debugging cycles and improving model reliability.

Key features include:

• Dual‑paradigm support: SimPy for process‑oriented, event‑driven modeling and PySD for stock‑and‑flow dynamics, both accessed through the same JSON schema.
• Iterative development: The LLM can ask clarifying questions, adjust parameters, and re‑run the model within a single conversation, with the server preserving context across turns.
• Robust analytics: Results come with statistical confidence intervals, warm‑up handling, and customizable metrics (wait times, throughput, etc.), enabling data‑driven decision making.
• Error guidance: When the JSON payload fails validation, the server returns human‑readable diagnostics that help the LLM or developer correct issues before re‑execution.

Typical use cases span academic research, business process optimization, and rapid prototyping of IoT or manufacturing systems. For example, a supply‑chain analyst can describe a new warehouse layout, receive simulation results on bottlenecks, and tweak parameters—all through a chat interface. In education, instructors can demonstrate simulation concepts to students by letting them interactively build models with natural language.

Integration into AI workflows is straightforward: the server registers as an MCP endpoint, and any LLM client that supports MCP can invoke it with a single prompt. The conversational nature of the interface means developers can embed simulation queries into larger application flows—such as an AI‑powered dashboard that continuously updates model predictions based on live data feeds. By lowering the barrier to entry for simulation modeling, Text2Sim MCP Server empowers developers to harness advanced analytical tools without deep expertise in discrete‑event or system dynamics frameworks.