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vosesoftware

ModelRisk MCP

Official
by vosesoftware

fit_all_data_and_wire

Fit marginal distributions and copula dependence from a data block, then wire the correlated simulation-ready model into the workbook in one reversible pass.

Instructions

ModelRisk: Fit BOTH the marginal distributions and the copula dependence from a data block, then wire the whole correlated, simulation-ready model into the workbook in one reversible pass. For each variable column it fits+ranks the best marginal (AIC/SIC/HQIC); across the columns it fits the best copula (fit_copula_to_data) and writes the correlated-U block at copula_anchor; each marginal is wired to its copula U so the inputs are dependent, not independent — capturing the tail co-movement a single correlation coefficient discards. Optionally runs a validating simulation. Defaults to dry_run=True (returns the exact planned formulas without writing). On any mid-build failure the whole change-set is rolled back. This is the data→model step no advisory agent can perform.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
runNoRun a validating simulation after wiring. Default False.
seedNo
sheetYesSheet holding the data and target cells.
columnsYesOne entry per data column, in column order: {'input_name': 'Demand', 'target_cell': 'F2'}.
dry_runNoPreview the planned formulas without writing. Default True.
samplesNo
workbookYesWorkbook file name.
criterionNoFit criterion: 'SIC' (default), 'AIC', or 'HQIC'.SIC
data_rangeYesRectangular data block, one column per variable, e.g. 'A2:C500'.
uncertaintyNoFit with parameter uncertainty. Default False.
target_sheetNoSheet holding the target cells and the copula block. Defaults to `sheet` (the data sheet) — pass this when the model lives on a different sheet than the data, e.g. data on 'Data', wired inputs on 'Model'.
copula_anchorNoTop cell for the correlated-U block. Omit to skip correlation.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
sheetYes
stepsYesOrdered log of what happened.
columnsYes
dry_runYes
workbookYes
simulatedYesWhether a validating simulation was run.
rolled_backNoTrue if a mid-build failure triggered a full rollback.
copula_anchorNoRange where the correlated-U block was written.
copula_familyNoBest-fit copula family wired across the columns, if any.
achieved_correlationNoRank correlation of the wired inputs from the validating run.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses key behaviors: dry run defaults to true, rollback on failure, reversible pass, and validation simulation. It does not cover all edge cases (e.g., permissions, idempotency) but provides solid context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose and each subsequent sentence adds detail (process, defaults, rollback). It is relatively long but efficient, with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (12 parameters, output schema exists), the description covers the overall workflow, dry run behavior, rollback, and validation. It complements the output schema sufficiently, leaving no major gaps for agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 83%, and the description adds value by explaining the process flow (fitting marginals per column, fitting copula, wiring). This helps interpret parameters like 'columns', 'copula_anchor', and 'target_sheet' beyond their schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool fits both marginals and copula dependence, then wires the model into the workbook. It uses specific verbs ('fit', 'wire') and resources ('marginal distributions', 'copula dependence'), clearly distinguishing from siblings like fit_copula_to_data or fit_and_rank_distributions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for complete model wiring ('data→model step') and mentions defaults and optional validation. However, it does not explicitly compare with siblings or state when to use this combined approach versus separate steps.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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