Simengine MCP server

SimEngine

A domain-agnostic Monte Carlo simulation and decision intelligence system.

Zero dependencies. Pure Python stdlib. Local web UI. Runs anywhere Python runs.

SimEngine turns uncertainty into numbers you can act on. Define your variables, your logic, and your risk tolerances in a JSON config. The engine runs thousands of simulated futures, scores them, and — if you ask — evolves toward the best strategy that still respects your safety constraints.

Think of it as a calculator for uncertainty.

Quick Start

# 1. Clone the repository
git clone <repo-url> ~/projects/simengine
cd ~/projects/simengine

# 2. Start the web UI
cd v3 && python3 server.py

# 3. Open your browser
#    http://localhost:8420

That is it. No pip install, no Docker, no Node, no build step. Python 3.8+ is the only requirement.

Install as an MCP server

uvx simengine-mcp

Listed on the official MCP registry. Hosted, no-install endpoint: join the waitlist.

Optional Native Acceleration

The default engine remains pure Python. A Rust workspace now lives at the repo root for optional acceleration of metric reducers, trajectory bands, risk evaluation, histograms, and native pre-sampling of simple numeric variable distributions.

This is opt-in and does not change the baseline workflow above.

Workspace layout

Cargo.toml
crates/
├── simengine-core/   # pure Rust numeric kernels
└── simengine-py/     # PyO3 extension module: simengine_native
v3/native.py          # optional loader + Python fallback glue

Build the native extension

cd crates/simengine-py
python3 -m venv ../../.venv
source ../../.venv/bin/activate
pip install maturin
maturin develop --release

After that, the existing Python engine will automatically use the native reducers and the native batch sampler when simengine_native is importable. If the module is missing or fails, SimEngine falls back to the existing Python implementation.

The batch sampler is conservative:

• it only activates for plain numeric variable params
• it stays off for correlated configs
• it can be disabled explicitly with SIMENGINEDISABLENATIVE_SAMPLING=1

This keeps the expression engine, accumulators, and metrics in Python while moving a larger chunk of the Monte Carlo loop into Rust.

There is also a narrower native fast path for stochastic expressions:

• binomial(n, p) can be delegated to Rust when the extension is installed
• it can be disabled explicitly with SIMENGINEDISABLENATIVEEXPRSAMPLING=1

This helps domains whose step_logic relies heavily on binomial(...) while still leaving general expression evaluation in Python.

On the Python side, repeated expressions are now compiled once and reused across Monte Carlo runs. That cache can be disabled explicitly with SIMENGINEDISABLEEXPR_CACHE=1 for benchmarking or troubleshooting.

The diagnostics module applies the same idea to its AST-only safe evaluator: parsed trees are reused across calls, and that cache can be disabled with SIMENGINEDISABLESAFEEXPRCACHE=1.

What It Does

Shortened. The full README is on GitHub.