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vosesoftware

ModelRisk MCP

Official
by vosesoftware

run_simulation

Run a Monte Carlo simulation on an Excel workbook, control iterations and seed, and save results to a .vmrs file for analysis.

Instructions

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. Blocks until the simulation completes. After this returns, call get_simulation_results — the produced .vmrs is automatically pinned as the active results source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoRandom seed for reproducibility (fixed seed).
samplesNoIteration count. Default: 1000. Either `samples` or `iterations` is accepted (they mean the same thing). If both are passed, `samples` wins.
save_toNoAbsolute path to write the .vmrs. Default: next to the workbook as `<book_stem>.vmrs`. For OneDrive-hosted workbooks (where path resolution can fail) the default falls back to the user's Desktop folder.
iterationsNoAlias for `samples`. ModelRisk's UI calls this 'samples'; many users call it 'iterations'. Both work.
workbook_nameNoWorkbook file name (e.g. 'model.xlsx'). Omit for the active workbook.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedYes
samplesYes
next_stepYesSuggested follow-up call for the MCP client — typically `get_simulation_results` to pull the per-output statistics.
vmrs_pathYes
workbook_nameYes
Behavior5/5

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

With no annotations, the description fully discloses: blocks until completion, matches UI behavior, uses fixed seed, saves to specific locations with fallback, and auto-pins result for next step. No contradictions.

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?

Description is moderately sized but every sentence adds value. Could be slightly more compact, but information density is high and well-structured with the main purpose first.

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?

Covers all essential aspects given complexity: purpose, behavior, defaults, output file, next step, and parameter nuances. Output schema exists, so return value disclosure is not needed. Highly complete.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds context beyond parameter descriptions: explains default for samples (1000), alias between samples and iterations, precedence rule, and save_to fallback for OneDrive. This aids correct parameter usage.

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?

Description clearly states 'Run a Monte Carlo simulation on the active (or named) workbook and save the results to a .vmrs file.' It specifies both the action and the resource, and distinguishes from sibling tools like get_simulation_results.

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?

Describes default behavior (1000 iterations, fixed seed), file location, and explicitly says to call get_simulation_results afterward. However, it does not explicitly mention when not to use this tool or compare to alternatives among siblings.

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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