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calculate_fire_target

Estimate the savings target needed for Financial Independence / Early Retirement. Runs Monte Carlo simulation and returns the probability the portfolio survives the planning horizon.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearsYesPlanning horizon in years.
currencyNoUSD
annual_expenseYesProjected annual spending in retirement (in selected currency).
inflation_rateNo
apply_inflationNo
current_savingsNoCurrent liquid + invested assets.
portfolio_volatilityNoAnnual return standard deviation %.
portfolio_return_rateNoExpected real annual return %.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observed

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description carries full burden. It does disclose the method (Monte Carlo simulation) and the return value (probability the portfolio survives the horizon), which is genuinely useful behavioral context. But it omits that results are stochastic/non-deterministic across runs and says nothing about compute cost or edge-case behavior.

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?

Two tight sentences, front-loaded with the purpose then the method/return. No wasted words, though it is short enough that the missing usage guidance isn't an accident of length.

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

Completeness3/5

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

For an 8-parameter stochastic tool with no annotations and no output schema, the description helpfully states the return value, partially compensating for the absent output schema. But it leaves sibling disambiguation and per-parameter semantics unaddressed, so it is only minimally complete.

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

Parameters2/5

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

Schema description coverage is 63%, and the description names no parameters at all. It adds zero meaning beyond the schema, leaving several inputs (e.g., inflation_rate, apply_inflation, currency enum) with no prose guidance to compensate for the coverage gap.

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+resource ('Estimate the savings target needed for Financial Independence / Early Retirement') so an agent knows exactly what is computed. However, it does not differentiate from the close sibling calculate_coast_fire, which an agent would reasonably confuse it with.

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?

No when-to-use guidance, prerequisites, or named alternatives are given. The presence of calculate_coast_fire as a near-identical FIRE calculator makes the absence of routing guidance a real gap.

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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