simulate_montecarlo
Read-only
Quantify uncertainty in a single random factor by running Monte Carlo simulation from parametric distributions. Returns summary statistics, percentiles, and histogram.
Instructions
Sample N draws from a parametric distribution and return summary statistics + percentiles + histogram. Use to quantify uncertainty around a single random factor: project NPV with uncertain growth rate, estimate latency tail percentiles, size insurance reserves. Supports normal/lognormal/uniform/triangular/beta/exponential. For multi-asset portfolio risk with correlations, use analyze_risk. Each call re-samples (non-idempotent). Capped at 2000 iterations on the free tier.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| distribution | Yes | Distribution family to sample from. | |
| params | Yes | Distribution parameters. Required keys depend on distribution: normal/lognormal={mean,stddev}, uniform={min,max}, triangular={min,mode,max}, beta={alpha,beta}, exponential={lambda}. | |
| simulations | No | Number of samples (default: 1000, max: 2000 free). |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| mean | Yes | ||
| stdDev | Yes | ||
| percentiles | Yes | ||
| histogram | No | Bucketed counts. | |
| iterations | Yes | ||
| executionTimeMs | No | ||
| timedOut | No |
Implementation Reference
- mission-control/apps/api/src/services/billing/tiers.ts:96-99 (registration)Billing configuration listing 'simulate_montecarlo' in the FREE_TOOLS array.
export const FREE_TOOLS = [ 'optimize_bandit', 'optimize_contextual', 'solve_schedule', 'score_convergence', 'plan_pathfind', 'simulate_montecarlo', ];