ModelRisk

ModelRisk: Build a time-series formula (VoseTimeGBM, VoseTimeAR1, etc.) and write it into the target_range. Time-series tools are exempt from the >50-cell bulk-write guard because the dimension of the time series is the whole point.

ModelRisk: Render a tornado chart of input sensitivity for a single output as a new sheet in the workbook. The sheet has a sorted data table (Spearman rank correlation + regression coefficient per input) plus a native Excel BarClustered chart with the largest-magnitude input at the top. Idempotent — if a sheet with the target name already exists, it's replaced. Useful when the user wants the visualization persisted in the workbook, not just returned over MCP.

ModelRisk: Split an output's uncertainty into EPISTEMIC (parameter / knowledge uncertainty — reducible with more data) and ALEATORY (natural variability — irreducible), via the law of total variance. ModelRisk has no two-dimensional-simulation worksheet function, so this approximates it from two runs you provide as two outputs: `total_output` from a full run (everything varying), and `conditional_output` from a run with the epistemic/parameter inputs FROZEN at point estimates (only natural variability left). Epistemic variance = total - aleatory. Tells you whether collecting more data (cuts ep…

ModelRisk: One-call workbook health check. Returns everything an MCP client typically wants at the start of a session: whether Excel is reachable, whether the ModelRisk SDK is activated, the active workbook's name + sheets, counts of inputs / outputs / distributions, whether a sibling `.vmrs` exists and when it was last modified, and the audit-log location. Use this as the first call instead of orchestrating 4-5 individual reading tools.

ModelRisk: Discover candidate input cells — numeric cells referenced by formulas — and rank them by how likely they are to be uncertain model inputs (vs. constants like 12 months per year). The ranking weighs reference count and number magnitude. Pair with propose_distributions_for_inputs.

ModelRisk: Heuristic discovery of cells that look like deterministic numeric inputs — i.e. a plain number, referenced by at least one formula. These are candidates for replacing with a distribution + VoseInput wrapper.

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…

ModelRisk: Fit several distribution families to a data range and rank them by goodness of fit. For each family it fits Vose<Family>FitObject and scores it with ModelRisk's information criteria — AIC, SIC (Schwarz/BIC) and HQIC — then ranks ascending (lower = better) by `criterion`. Families with no fit function, or that can't fit the data, are returned under `skipped` with a reason. Use this instead of guessing a single family for fit_distribution_to_data. Runs on a transient scratch sheet that is always deleted; the data is not modified.

ModelRisk: Fit parametric copulas to a multi-column data range and rank them by goodness of fit — the DEPENDENCE STRUCTURE fitted from data, not merely constructed. Tries Normal, T, Clayton, Frank and Gumbel families (Vose<Family>FitObject), scores each with AIC / SIC / HQIC, and reports the winner plus its tail-dependence character (lower = crash-together, upper = boom-together, both = T, none = Normal/Frank) — the joint-tail risk a single correlation coefficient throws away. Runs on a transient scratch sheet that is always deleted; the data is not modified. Pair with fit_all_data_and_wire to…

ModelRisk: Fit a distribution family to a data range and write the result. 'family' is e.g. 'Normal', 'Lognormal', 'Beta', 'Gamma', 'Weibull' — the tool maps it to the catalogue function (Vose<Family>Fit). Set uncertainty=True (the default) to include parameter uncertainty in the fitted distribution.

ModelRisk: Fit an extreme-value / Generalised-Pareto tail to data and read its risk. `family` is 'GPD' (peaks-over-threshold, the standard tail model), 'GEV' (block maxima), 'ExtValueMax', or 'ExtValueMin'. For GPD peaks-over-threshold, pass the range of exceedances above your threshold as `data_range`. Writes a Vose<Family>FitObject (dry_run previews) and returns the fitted tail's mean and high percentiles (P95 / P99 / P99.5 / P99.9) computed analytically — the tail risk without a simulation. Feed the written object cell to compute_distribution / get_tail_risk for more.

ModelRisk: Fit TIME-SERIES models to a historical data range and rank them by goodness of fit — GBM (plus mean-reverting / jump-diffusion / seasonal variants), AR1/AR2, MA1/MA2, ARMA, ARCH/GARCH/EGARCH. Each family's VoseTime<Family>FitObject is scored with AIC / SIC / HQIC on a transient scratch sheet (nothing in the workbook is modified by the ranking). Optionally pass `target_range` (+ dry_run=False) to write the best model's projection — a VoseTime<Family>Fit(...) ARRAY formula, one period per cell, CSE-entered — so each simulated path carries the fitted dynamics (autocorrelation, volatili…

ModelRisk: Generate an executive-audience summary of the most recent simulation results for a workbook. Returns markdown ready to paste into a deck/report — covers deterministic vs P50 vs mean comparisons, P80 contingency, and the top sensitivity drivers.

ModelRisk: Get the name and path of the active workbook.

ModelRisk: Read a single cell's formula, value, and number format.

ModelRisk: Pearson and Spearman rank correlation between the named simulation inputs and outputs. Computed from the per-iteration samples ModelRisk records. Pass a name list to restrict; otherwise all variables are included.

ModelRisk: Return raw per-iteration sample values for a single output or input. Useful for custom histograms, arbitrary percentiles, downstream analysis. Caps at 10 000 samples by default to keep the MCP response small; raise `max_n` if you need more (a 100 000-iteration sim returns ~100 KB of JSON at max_n=100000).

ModelRisk: Tornado / sensitivity ranking for a single output. Returns each input ranked by its Spearman rank correlation with the output, plus the standardised regression coefficient.

ModelRisk: Read simulation result statistics for the workbook's outputs. Returns per-output mean, stdev, variance, skewness, kurtosis, min, max, and P5/P10/P25/P50/P75/P90/P95 percentiles. Requires a simulation to have been run; pass output_names to filter.

ModelRisk: Tail-risk profile of a simulation output from its per-iteration samples — Value-at-Risk (VaR) and Conditional VaR / expected shortfall (CVaR) at each confidence level, plus optional threshold probabilities P(X>t) / P(X<=t). `tail='upper'` (default) treats large values as the loss (e.g. cost, claims); `tail='lower'` treats small values as the loss (e.g. NPV, profit). VaR is the alpha-quantile; CVaR is the mean of the worst (1-alpha) tail beyond it. Reads the samples ModelRisk recorded in the .vmrs — run a simulation first.

ModelRisk: Aggregated summary of a workbook — sheet names plus counts of VoseInput, VoseOutput, distribution, formula, and numeric cells. One-shot alternative to running the individual list tools.

ModelRisk: Write a Vose distribution formula (e.g. =VoseModPERT(...)) into a cell. Validates the function name against the ModelRisk catalogue. Defaults to dry_run=True — Claude must explicitly pass dry_run=False to commit.

ModelRisk: List every cell containing a Vose distribution / aggregate / copula / time-series / fitting function. Includes flags for whether each cell is also wrapped with VoseInput or VoseOutput. Optional `sheet` restricts the scan to one sheet.

ModelRisk: List every cell wrapped with VoseInput() in the workbook. Returns each cell's reference, the input name, the full formula, and the current calculated value.

ModelRisk: List every cell wrapped with VoseOutput() in the workbook.

ModelRisk: List all Excel workbooks currently open.

ModelRisk: List every variable in the active simulation results (`.vmrs`) that's also declared as a VoseInput or VoseOutput in the workbook. Each entry: `{name, kind, var_id, iterations}`. Use this before `get_samples` or `read_vmrs` when you don't already know which outputs / inputs exist in the file.

ModelRisk: Open a workbook (.xlsx/.xlsm) from disk in the running Excel so the other tools can act on it. Pass an absolute file path. If a workbook with the same file name is already open, returns that one (Excel won't open two with the same name). Requires Excel running.

ModelRisk: One-call blueprint for turning a deterministic workbook into a Monte Carlo risk model. Reports what's already there (declared outputs, existing distributions), the ranked hard-coded cells that look like uncertain inputs, and an ordered, state-aware checklist of next actions (wrap outputs, fit/propose distributions, correlate, audit, simulate, interpret). Read-only — it plans, it doesn't modify. Run this first when asked to 'add uncertainty' or 'make this a risk model'.

ModelRisk: Propose distribution families for a list of uncertain inputs. Each input gets a ranked list of recommendations from the methodology-grounded selection guide. The tool does NOT write to Excel — it returns suggestions for the LLM to walk through with the user before committing via replace_constant_with_distribution.

ModelRisk: Read a contiguous range as a 2D array of values and formulas. Use ranges like 'A1:C10'.

ModelRisk: Read simulation results directly from a `.vmrs` file. Convenience wrapper for `set_active_vmrs` + `get_simulation_results` that doesn't need an open workbook. Pass `output_names` to filter; leave empty to attempt enumeration of all known outputs.

ModelRisk: Replace a hard-coded number in a cell with a Vose distribution wrapped by VoseInput. Use after find_hard_coded_inputs identifies candidates. This is the only tool that overwrites a non-Vose cell — it does so by design.

ModelRisk: Restore a cell to its pre-write state from the audit log. Reads %LOCALAPPDATA%\VoseSoftware\modelrisk-mcp\writes.log and rewrites the oldest captured before-formula for the cell. Pass `since` (ISO timestamp) to restrict the window.

ModelRisk: Recover a workbook that's been left in a 'frozen sample' state — VoseOutput cells stuck on per-iteration sample values instead of their deterministic baseline. Triggers a full Excel recalculation (Application.CalculateFull) which re-evaluates every formula and restores the deterministic values. Use this after `run_simulation` raises a post-condition error, or whenever `list_modelrisk_outputs` shows nonsense `current_value`s that look like a single sample draw rather than the model's deterministic answer.

ModelRisk: Reverse stress test — start from a BAD output outcome and work back to the joint input state that produces it. Partitions the simulation's iterations into breach / no-breach (output above/below a threshold, given directly or as a percentile), then for each input reports how far its mean shifts inside the breach set (in its own standard deviations) and how concentrated breaches are in its tail — a breach-driver tornado — plus the mean input vector as a concrete named stress scenario. This is the Solvency II / PRA 'reverse stress test' and is only possible with the engine's recorded p…

ModelRisk: Sweep a single input cell across multiple deterministic values, running a full simulation at each. Returns per-output P5 / P50 / P95 / mean for every scenario value. Useful for what-if analysis: 'what if widget cost is $50 vs $75 vs $100'. The cell's original formula is captured before the sweep and restored afterwards (even on error), so the workbook ends in its pre-call state. Each scenario takes roughly the same time as one `run_simulation` call, so keep the values list short — 3-7 scenarios is a normal range.

ModelRisk: Run a Monte Carlo simulation on the active (or named) workbook and save the results to a `.vmrs` file. Defaults to 1000 iterations with a fixed seed for reproducibility, and saves the .vmrs next to the workbook as `<book>.vmrs`. The simulation is run via the same XLL commands ModelRisk's own ribbon uses (VoseStartSimulCustom12 + VoseGetDataSZ12 with the SaveResultsToFile session), so behaviour matches what you'd see clicking 'Simulate' manually. ModelRisk simulates the active workbook, so a named workbook is made active first. The saved .vmrs is checked to hold this run (workbook, s…

ModelRisk: Save the workbook to a specific path on disk. Distinct from the user's Ctrl+S — the MCP server never calls Workbook.Save() implicitly. Use only when the caller explicitly named a target file. Refuses to overwrite an existing file unless overwrite=True. Returns the resolved absolute path that was written.

ModelRisk: Pin a specific `.vmrs` file as the source of simulation results. Pass the absolute path of the file; subsequent calls to get_simulation_results / get_correlation_matrix / get_sensitivity_ranking will read from it instead of trying to locate a sibling file next to the workbook. Pass an empty string to clear the override.

ModelRisk: Create or overwrite a workbook-level named range. Useful for giving cells clear identities the LLM can reference by name later. The reference must be A1-style (e.g. 'Sheet1!$A$1:$A$10').

ModelRisk: Wrap an existing distribution cell with VoseInput("name")+ so it appears in the input list and the Results Viewer.

ModelRisk: Wrap an existing output cell with VoseOutput("name")+ so it appears in the output list and Results Viewer.

ModelRisk: Write an arbitrary formula or value into a single cell. Use this for wiring cells (e.g. =A1*B1, =SUM(...), =IF(...), or links to other sheets) that aren't covered by the Vose-specific tools. Safety: refuses to overwrite a cell containing an existing formula unless allow_overwrite=True — that protects user-written formulas AND prior Vose distributions. Empty cells write freely. Defaults to dry_run=True; pass dry_run=False to commit. Every commit appends to the audit log so `restore_cell` can roll back.