Turn a min–max guess into distribution parameters
convert_range_to_distributionTurns a low-to-high guess, such as "between 80 and 120, 90% sure", into the exact parameters a model input needs. A distribution is the shape of the uncertainty, for example normal (a bell curve), log_normal (skewed and never negative), triangle or uniform. The low and high values are the edges of the range you are that sure of, not hard limits. Use this when you have a rough estimate and need distribution parameters instead of guessing them. Its reverse is convert_distribution_to_range. It returns the distribution type and its parameters, ready to use as a model input's distribution_params. The distribution_type parameter explains which type fits which kind of quantity. Supported types: normal, log_normal, uniform, triangle, beta, gamma, exponential, binomial, discrete_normal, discrete_lognormal, discrete_gamma, constant and bernoulli. Not supported: poisson, categorical, dirichlet, multivariate_normal and constant_array. For beta, give low and high as fractions (2% is 0.02). Cost: free; uses no credits and no free runs.
Tool call template
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"distribution_type": "<distribution_type>"
},
"name": "convert_range_to_distribution"
}
}Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| max_estimate | No | The high end of your range. Required for every type except constant and bernoulli. | |
| min_estimate | No | The low end of your range. Required for every type except constant and bernoulli. Exponential ignores it, but a value must still be sent (any number works). | |
| mode_estimate | No | The most likely value. Required for constant (the fixed value), bernoulli (the chance of success, from 0 to 1) and binomial. For triangle it sets the peak; it must lie strictly between low and high, and defaults to their midpoint. For beta it is used as the peak when a fit allows, but the low and high ends take priority. Ignored by other types. | |
| percentile_range | No | A single fraction: the share of outcomes that should fall between the low and high ends, which is how sure you are. The default 0.9 means the middle 90%: a 5% chance of being below the low end and 5% above the high end. Mapping hedged words: "very sure" or "almost certainly" is about 0.9; "probably" is about 0.8; if unsure, ask the user. "Could be higher" means pick a right-skewed type such as log_normal, not a wider range. | |
| distribution_type | Yes | The distribution to fit. Which to pick: a rate or proportion between 0 and 1 (conversion, churn) → beta, with low and high given as fractions (2% is 0.02). A positive amount that can run high (revenue, price, deal size) → log_normal. A quantity equally likely to land above or below its middle → normal. A value with hard limits you know → uniform (flat) or triangle (most likely value in between). A whole-number count (customers, units) → discrete_normal, discrete_lognormal or discrete_gamma, or binomial for successes out of a fixed number of attempts. Not supported here: poisson, categorical, dirichlet, multivariate_normal and constant_array. |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Your inputs, echoed back. | |
| billing | No | Who you are and what this call cost: principal, auth source, credits debited and balance, or the anonymous allowance left today. | |
| parameters | Yes | The fitted parameters, for example {"mean": 100, "std": 12.2}. | |
| distribution_type | Yes |