lowriskquotes-montecarlo

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.

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.

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.

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.

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.

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.

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

Annotations already establish readOnlyHint=true, destructiveHint=false and openWorldHint=false, which matches a pure simulation. The description adds real behavioral context beyond that: the output is a stochastic probability plus percentiles, it reports the assumptions used (real returns, annual steps), and a disclaimer is attached. It does not discuss runtime cost or iteration trade-offs, keeping it short of a 5.

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

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

The core sentence front-loads the simulation name, its inputs and its outputs; the second sentence carries the necessary disclaimer. Slightly dense but no filler, and the most important information comes first.

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

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

With no output schema, the description does the work of explaining what comes back (probability, percentiles, assumptions), and annotations cover the safety profile. It is close to complete for this tool; only the sibling relationship and any determinism note are left implicit.

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

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

Schema coverage is 100% — all six parameters carry descriptions and the four required ones are named in the description. The description adds no format, unit or range detail beyond what the schema already provides, so the baseline 3 applies.

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

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

The description names a specific verb and resource ('Monte Carlo retirement drawdown simulation') and enumerates the inputs and outputs (probability money lasts, end-balance percentiles, assumptions). It is clear on its own, but it never mentions the sibling monte_carlo_estimate, so an agent must infer the boundary between the two.

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

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

There is no explicit when-to-use or when-not-to-use guidance, and the sibling monte_carlo_estimate is not referenced as an alternative. The 'educational illustration, not personal financial advice' caveat is a disclaimer about the output's nature, not routing guidance for tool selection.

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