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scenariosim-mcp

by io.github.inity13

sensitivity_analysis

Vary one or more input assumptions and show the impact on a target output metric (one-at-a-time sensitivity). Provide 'template', the input to sweep via 'variable' (or 'variables' array), and 'target_metric' (defaults to the template's primary output). Control the sweep with 'variation' (fractional +/- around the baseline, default 0.2), 'steps' (default 5), or explicit 'values' / 'min'+'max'. Returns per-variable sweeps, an elasticity estimate, the output range, and a ranking of the most influential inputs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxNoSweep upper bound (with 'min').
minNoSweep lower bound (with 'max').
stepsNoNumber of sweep points per variable (2-200).
inputsNoScenario assumptions as {name: value}. Which keys are valid depends on the template (call list_templates). Unlisted keys fall back to documented defaults; unknown keys are ignored and reported in notes. You may also pass assumptions at the top level.
valuesNoExplicit sweep values for a single 'variable'.
horizonNoNumber of periods to project forward (1..1200). Default depends on template (usually 12).
templateYesPre-built scenario template id: saas_growth, pricing_change, churn_impact, cost_reduction, hiring_plan, cash_runway, unit_economics, marketing_funnel, compound_growth (aliases like 'saas','pricing','runway','ltv' also resolve). Omit (or use 'custom') to run a free-form 'metrics' projection.
variableNoA single input name to sweep.
variablesNoMultiple inputs to sweep (one at a time). Each: {name, variation?|values?|min?+max?, steps?}.
variationNoFractional sweep around the baseline (0<v<=1). 0.2 = +/-20%.
period_labelNoLabel for each period; also sets annualization (periods/year).month
target_metricNoOutput metric to track (see a template's 'outputs' via list_templates). Defaults to the template's primary output.

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are present, so the description carries the burden. It discloses defaults (variation=0.2, steps=5), control options, and return contents (per-variable sweeps, elasticity, range, ranking). It does not explicitly state read-only behavior, but the analysis-focused wording implies no mutation.

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 single paragraph is dense but well-structured: purpose, usage, controls, then outputs. It is slightly long but every sentence adds value, so it earns a strong score.

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 12 parameters, nested objects, and no output schema, the description is remarkably complete. It covers template selection, sweep mechanics, defaults, and return values. Minor details like alias handling are left to the schema.

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%, but the description adds substantial meaning beyond field descriptors: it explains how to combine 'template', 'variable/variables', 'target_metric', and 'variation/steps/values/min-max' into a coherent workflow. This significantly exceeds the baseline.

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 states a specific verb and resource: 'Vary one or more input assumptions and show impact on target output metric.' It clearly distinguishes from siblings like run_scenario or compare_scenarios by emphasizing one-at-a-time sensitivity.

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?

Provides clear usage context: specify template, variable(s), target_metric, and control via variation, steps, values, or min/max. It does not explicitly mention when not to use it or alternative tools, but the one-at-a-time distinction is implied.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct operation: health_check for status, list_templates for discovery, run_scenario for single simulations, compare_scenarios for multi-scenario comparison, sensitivity_analysis for input sweeping, and break_even for root-finding. There is no meaningful overlap or ambiguity between tool purposes.

Naming Consistency5/5

All tool names follow a clear snake_case pattern, with most using verb_noun construction (run_scenario, list_templates, compare_scenarios). Even though health_check and break_even are compound nouns, they are consistent in style and clearly action-oriented.

Tool Count5/5

Six tools is well within the ideal range and each tool fills a necessary role for scenario simulation. The set covers running, comparing, sensitivity analysis, break-even solving, discovery, and system health without being bloated or sparse.

Completeness5/5

The tool surface covers the full lifecycle of scenario analysis: discover templates, run a scenario, compare multiple, vary inputs, and solve for targets. Custom scenarios are also supported via run_scenario, so there are no critical gaps in the domain.

Resources