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Monte Carlo cost estimate

monte_carlo_estimate
Read-only

Run a three-point (triangular) Monte Carlo simulation over cost line items. Use when a user needs a realistic range for a quote, budget or project cost instead of a single guess. Returns total-cost percentiles (p10/p50/p80/p90) and per-item breakdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNooptional seed for reproducible output
itemsYesCost line items with three-point estimates
iterationsNooptional, 100-20000, default 5000

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description goes beyond them by disclosing the simulation method (three-point triangular) and the return shape (p10/p50/p80/p90 percentiles plus per-item breakdown), which is genuinely additive.

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?

Three tight sentences ordered as what-it-does, when-to-use, what-it-returns. Minor redundancy in repeating 'cost' across clauses, but nothing that wastes an agent's attention.

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

Completeness4/5

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

With no output schema, the description compensates by naming the returned percentiles and per-item breakdown. The only omissions are iteration-range defaults and the determinism implications of seed, both of which the schema already carries.

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 low/likely/high, seed and iterations are all documented in the schema itself. The description adds only that the items are three-point estimates, so the baseline 3 applies.

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 a specific verb and resource ('Run a three-point (triangular) Monte Carlo simulation over cost line items') and even names the distribution type. It does not explicitly distinguish itself from the sibling retirement_drawdown, so it stops short of a 5.

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?

Gives a clear triggering condition ('Use when a user needs a realistic range for a quote, budget or project cost instead of a single guess'), which routes the agent well. It names no when-not condition or explicit alternative tool, so it is not a 5.

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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