simulate_desLABSCO SUMMARY
The validation tools are what make conversational modelling work.
A model built by dialogue gets things wrong, repeatedly, and the difference between usable and frustrating is whether the error says which field is malformed and what it should look like. Three validation modes plus contextual schema help is a lot of surface pointed at exactly that. Replications with confidence intervals matter for the same reason — a single run of a stochastic model is an anecdote, and the tool that returns variability is the one you should be calling.
What it is
A simulation engine behind an MCP interface. Models are JSON, validated against JSON Schema 2020-12, and executed by SimPy for discrete-event work or PySD for stock-and-flow system dynamics.
What you get
- 16 tools across five categories
- simulate_des runs a discrete-event model with entity types, resources and processing rules; simulate_sd runs a PySD-compatible abstractModel
- run_multiple_simulations executes independent replications and returns confidence intervals, variability measures and a reliability score, with seed control for reproducible runs
- validate_model checks a configuration in partial, strict or structural mode and auto-detects which schema applies; help_validation and get_schema_help explain what failed and how the schema is shaped
- save_model, load_model, export_model and delete_model manage models with metadata, version conflict resolution and token-count estimates for export
- list_templates, load_template, save_template and delete_template cover prebuilt and personal starting points
- get_sd_model_info analyses a system-dynamics model's structure without running it; convert_vensim_to_sd_json translates Vensim .mdl files into the PySD JSON format
- Resources support FIFO, priority and preemptive queuing; entity behaviour covers balking, reneging and resource failure cycles
Requirements
Python 3.12 or higher and uv. Clone the repository, run uv sync, and launch with uv run python -m mcp_server. No account and no key. Distributions are parsed with regular expressions rather than evaluated, so a model configuration never becomes executable code.
Setup effort
Build from source — git clone https://github.com/IamCatoBot/text2sim-MCP-server.git
USE CASES
01
Turning a described process into a runnable model
Entity types, resources and processing steps go in as JSON and come back as results with metrics.
02
Trusting a result from a stochastic model
Multiple seeded replications with confidence intervals instead of one run.
03
Modelling stocks and flows over time
PySD-compatible models run directly, and their structure can be analysed before execution.
04
Bringing an existing Vensim model in
Conversion to the PySD JSON format with format validation on the way through.
05
Not rebuilding the same model next week
Save with metadata, reload by name, and export in a form that fits back into a conversation.
16 tools
Listed in project docs. Your client may show a different list.
simulate_sdrun_multiple_simulationsvalidate_modelhelp_validationget_schema_helpsave_modelload_modelexport_modellist_templatesload_templatesave_templatedelete_templateget_sd_model_infoconvert_vensim_to_sd_jsondelete_modelBEFORE YOU INSTALL
What to know first
Tools you get
16 tools named in the project's documentation.
Requires
- uv on PATH
- Python 3.12 or later
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
This one is built from source rather than installed with a single command.
Server setup
TERMINAL
git clone https://github.com/IamCatoBot/text2sim-MCP-server.git
cd text2sim-MCP-server
uv syncMCP Clients
Claude Desktop
CLAUDE DESKTOP · JSON
{
"mcpServers": {
"Text2Sim MCP Server": {
"command": "uv",
"args": [
"--directory",
"<YOUR_PATH_TO_TEXT2SIM_MCP_SERVER>",
"run",
"python",
"-m",
"mcp_server"
],
"env": {}
}
}
}<YOUR_PATH_TO_TEXT2SIM_MCP_SERVER> is the full path of the text2sim-MCP-server folder the clone created. If the client cannot find uv, use the full path that which uv prints as "command".
