Monte Carlo tools: three-point cost estimation and an educational retirement drawdown simulator.
Average 4/5 across 2 of 2 tools scored.
The two tools target clearly different scenarios: cost/quote estimation versus retirement drawdown. Even though both use Monte Carlo methods, the descriptions make the intended use unambiguous.
Both names are lowercase snake_case noun phrases and share a descriptive, domain-first style. They do not follow a verb_noun pattern, but the naming convention is internally consistent.
With only two tools, the server is on the thin side and the purpose feels narrowly scoped. Each tool is substantial and earns its place, but the count is borderline.
Each tool provides a complete self-contained simulation with clear outputs and assumptions, so there are no obvious dead ends. The overall surface is small, but it covers the two advertised Monte Carlo scenarios well.
monte_carlo_estimateAInspectRun 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.
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | optional seed for reproducible output | |
| items | Yes | Cost line items with three-point estimates | |
| iterations | No | optional, 100-20000, default 5000 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It explains the return shape (percentiles and per-item breakdown) and the triangular methodology, but it does not mention that results are stochastic, that a seed controls reproducibility, or that the operation has no side effects.
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?
Two sentences with no wasted words: the first states the action and domain, the second provides usage guidance and return-value summary. The key information is front-loaded and every sentence earns its place.
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?
Given the lack of an output schema, the description appropriately discloses the main return values. It covers purpose, usage context, and output shape, and the schema covers parameters. It is slightly incomplete in not mentioning randomness/reproducibility, but overall it is sufficient for correct invocation.
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 the schema already documents each parameter. The description adds useful context by clarifying the three-point estimate nature and the output percentiles, but it does not substantially extend the parameter-level meaning beyond the schema.
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 states a specific verb and resource: 'Run a three-point (triangular) Monte Carlo simulation over cost line items.' It clearly identifies the tool's purpose and scope, and the focus on cost estimates for quotes, budgets, or projects distinguishes it from the only sibling tool, retirement_drawdown.
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?
The description gives an explicit use case: 'Use when a user needs a realistic range for a quote, budget or project cost instead of a single guess.' It provides clear context for when to invoke the tool, though it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
retirement_drawdownAInspectMonte Carlo retirement drawdown simulation: given a portfolio, annual spending, horizon and equity allocation, returns the probability the money lasts, end-balance percentiles and the assumptions used (real returns, annual steps). Educational output only — always present it with its assumptions and disclaimer, never as personal financial advice.
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | optional seed for reproducible output | |
| years | Yes | horizon in years, 1-80 | |
| equityPct | Yes | equity share of portfolio, 0-1 | |
| portfolio | Yes | starting balance (> 0) | |
| iterations | No | optional, 100-20000, default 5000 | |
| annualSpend | Yes | withdrawal per year (>= 0) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that this is a Monte Carlo simulation, that output is educational only, and that assumptions (real returns, annual steps) are included. It does not mention nondeterminism or the role of seed, but for a pure simulation tool this is mostly adequate.
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?
Two tight sentences: the first states purpose and outputs, the second adds the critical educational caveat. No filler, no repetition of schema content, and important constraints are front-loaded.
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?
There is no output schema and no annotations, so the description must explain return values itself; it does so by naming probability, end-balance percentiles, and assumptions. The educational-output disclaimer is also included. It could be more explicit about output format and reproducibility, but it is largely complete for the tool's complexity.
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 the schema already documents all parameters. The description conceptually maps portfolio, annualSpend, years, and equityPct but adds no detail about seed, iterations, ranges, defaults, or constraints beyond what the schema provides. Baseline 3 is appropriate.
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?
Description opens with a specific verb and resource: 'Monte Carlo retirement drawdown simulation.' It enumerates inputs (portfolio, annual spending, horizon, equity allocation) and outputs (probability of lasting, percentiles, assumptions). However, it does not differentiate from sibling monte_carlo_estimate, so an agent cannot immediately tell which tool to choose.
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?
Provides clear context: output is educational only and must always be presented with assumptions and disclaimer. This is useful usage guidance. But it does not explicitly mention when to prefer this tool over monte_carlo_estimate or state any exclusions.
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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