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by thyn-ai
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TypeScript
Hybrid

simulate

Run Monte Carlo simulations to quantify decision risk and receive a structured recommendation with expected value, probability of loss, and confidence percentiles.

Instructions

Run a Monte Carlo simulation and get a structured decision recommendation. Use for: quantifying risk in a decision, comparing expected outcomes, getting probability-weighted recommendations. Synchronous deterministic compute governed by the plan's per-minute rate limit and monthly quota (429 on excess); the run is recorded asynchronously and appears in list_runs. Returns the decision envelope: recommended_action, expected_value, probability_of_loss, confidence, percentiles, and run metadata (run_id, execution_ms, scenarios_run). Which to use: the full decision-envelope analysis on ONE scenario; use compare for named scenario bake-offs, recommend for a ranked pick among actions, score for a single weighted number, plan_decision for just the plan summary, or product_decision for plain-English inputs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoauto = minimal setup; expert = full distribution controlauto
objectiveNoAuto-mode objective. For expert mode, use objective_function.maximize_net_value
variablesYesInput variables as triangular distributions (low, most-likely, high)
n_simulationsNoMonte Carlo iteration count. Auto mode accepts 100–100,000; expert mode accepts 100–1,000,000.
objective_functionNoExpert-mode expression, for example 'revenue - cost'. Required when mode='expert'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations carry no behavioral hints, so the description takes full responsibility. It discloses synchronous deterministic execution, rate limits and quota with the exact 429 error, asynchronous recording into list_runs, and the full return envelope. This is rich, non-obvious context an agent needs.

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

Conciseness5/5

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

Despite its length, every sentence earns its place: purpose, use cases, execution behavior, return fields, and routing to alternatives. Key facts are front-loaded and the sibling comparison is organized into a single compact paragraph.

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?

For a 5-parameter tool with no output schema, the description compensates by specifying return fields, execution mode, rate-limit consequences, and the async side effect. Combined with 100% schema coverage, nothing critical is missing for correct invocation.

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 description coverage is 100%, so the schema already documents all 5 parameters with types, defaults, enums, and expert-mode requirements. The description adds no additional parameter-level meaning beyond reiterating the decision envelope, which is a fair baseline of 3.

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?

States a specific verb and resource ('Run a Monte Carlo simulation') and describes the concrete output ('structured decision recommendation'). It explicitly contrasts itself with sibling tools like compare, recommend, score, plan_decision, and product_decision, so an agent can distinguish it without opening schemas.

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

Usage Guidelines5/5

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

Begins with 'Use for:' enumerating three clear use cases. The 'Which to use' section explicitly names each sibling alternative and the condition selecting it, giving both when-to-use and when-not-to-use guidance.

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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