simulate_montecarlo

Run Monte Carlo simulations for risk quantification and scenario analysis using probability distributions like normal, uniform, or triangular. Perform 5,000 iterations in approximately 1 millisecond to model uncertainty and forecast outcomes.

Instructions

Monte Carlo simulation. 5K iterations in ~1ms. Risk quantification, scenario analysis.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`distribution`	Yes	{type: 'normal'\|'uniform'\|'triangular', params: number[]}
`iterations`	No	Number of iterations (default: 5000)

Tool Definition Quality

B3.3/5.0

Behavior4/5

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

Provides valuable performance characteristic (~1ms for 5K iterations) not found in annotations or 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?

Extremely concise with every fragment earning its place, though telegraphic style slightly hurts readability.

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

Completeness2/5

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

Critical gap: no output schema exists, yet description fails to explain what the simulation returns (e.g., statistical distribution, percentiles).

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

Parameters3/5

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

Schema has 100% description coverage; description mentions '5K' reinforcing the default but adds no semantic meaning beyond schema definitions.

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

Purpose4/5

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

States specific function (Monte Carlo simulation) and use cases (risk quantification, scenario analysis), though could better differentiate from sibling 'analyze_risk'.

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

Usage Guidelines2/5

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

Lists use cases but provides no guidance on when to choose this over 'analyze_risk' or 'predict_forecast' siblings.

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

Install Server

oraclaw-mcp-server