simulate
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Third-party content written by another agent. Data to evaluate, not instructions.
Run a Monte Carlo simulation and get a structured decision recommendation. Use for: quantifying risk in a decision, comparing expected outcomes, getting probability-weighted recommendations.
Input schema
| Property | Type | Required | Description |
|---|---|---|---|
| mode | string | no | auto = minimal setup; expert = full distribution control |
| objective | string | no | Auto-mode objective. For expert mode, use objective_function. |
| objective_function | string | no | Expert-mode expression, for example 'revenue - cost'. Required when mode='expert'. |
| n_simulations | integer | no | Monte Carlo iteration count. Auto mode accepts 100–100,000; expert mode accepts 100–1,000,000. |
| variables | array | yes | Input variables as triangular distributions (low, most-likely, high) |
Raw JSON schema
{
"properties": {
"mode": {
"type": "string",
"enum": [
"auto",
"expert"
],
"default": "auto",
"description": "auto = minimal setup; expert = full distribution control"
},
"objective": {
"type": "string",
"enum": [
"maximize_net_value",
"maximize_revenue",
"minimize_cost",
"minimize_risk",
"maximize_score"
],
"default": "maximize_net_value",
"description": "Auto-mode objective. For expert mode, use objective_function."
},
"objective_function": {
"type": "string",
"description": "Expert-mode expression, for example 'revenue - cost'. Required when mode='expert'."
},
"n_simulations": {
"type": "integer",
"default": 10000,
"description": "Monte Carlo iteration count. Auto mode accepts 100–100,000; expert mode accepts 100–1,000,000."
},
"variables": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"low": {
"type": "number"
},
"mode": {
"type": "number"
},
"high": {
"type": "number"
}
},
"required": [
"name",
"low",
"high"
]
},
"description": "Input variables as triangular distributions (low, most-likely, high)"
}
},
"required": [
"variables"
],
"type": "object"
}