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QuantCalc Retirement Engine

by app.quantcalc
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Compare published return assumptions

compare_return_assumptions
Read-only

Runs the same plan against several published capital market assumption sets and returns the success rate and median outcome under each, showing how far the answer moves with the return forecast used. Every input is optional: omitted plan inputs use an illustrative plan (age 65, $1,000,000 saved, $40,000 a year spending) which the reply states in its first line.

Input Schema

NameRequiredDescriptionDefault
pensionNoYearly pension income. Default 0.
sourcesNoSource ids to compare. Defaults to all.
allocationsNoPercentages summing to 100: [US stocks, international stocks, bonds, real estate, cash]. Default [60,10,25,5,0].
current_ageNoCurrent age of the primary person.
bucket_yearsNoOnly with withdrawal_strategy bucket: years of spending held in the cash bucket, 1-10.
return_modelNoHow returns are generated. parametric (default): the forward-looking published assumptions. bootstrap: stationary block bootstrap of historical months. historical: replays real months from 1926 in order. The last two need a PRO licence; without one the engine runs parametric and the result says so.
ss_start_ageNoAge Social Security starts. Default 67.
inflation_rateNoAnnual inflation as a percent, e.g. 2.5. Default 2.5.
retirement_ageNoAge work income stops. Defaults to current age.
returns_sourceNoWhich published return set to use (see list_return_assumption_sources). Default jpmorgan.
annual_spendingNoPlanned yearly spending in today's dollars, before income tax (this tool runs the standard engine, which does not model tax).
current_savingsNoTotal invested portfolio today, in dollars.
life_expectancyNoAge the plan must last until. Default 92.
social_securityNoYearly Social Security in today's dollars. Default 0 — and 0 is reported as an explicit assumption, not hidden.
pension_start_ageNoAge the pension starts. Default 65.
withdrawal_strategyNoSpending rule. fixed (default): the planned amount, inflation-adjusted, every year. guyton-klinger: guardrails that cut or raise spending as the portfolio moves. vpw: variable percentage withdrawal. bucket: a cash reserve of bucket_years of spending.
monthly_contributionNoMonthly savings until retirement. Default 0.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / required
      Previous value: -[
-  "current_age",
-  "current_savings",
-  "annual_spending"
-]New value: +[]
  2. Changed3 schema fields changed
    • addedInput schema / properties / bucket_years
      Added value: +{
+  "description": "Only with withdrawal_strategy bucket: years of spending held in the cash bucket, 1-10.",
+  "type": "integer"
+}
    • addedInput schema / properties / return_model
      Added value: +{
+  "description": "How returns are generated. parametric (default): the forward-looking published assumptions. bootstrap: stationary block bootstrap of historical months. historical: replays real months from 1926 in order. The last two need a PRO licence; without one the engine runs parametric and the result says so.",
+  "enum": [
+    "parametric",
+    "bootstrap",
+    "historical"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / withdrawal_strategy
      Added value: +{
+  "description": "Spending rule. fixed (default): the planned amount, inflation-adjusted, every year. guyton-klinger: guardrails that cut or raise spending as the portfolio moves. vpw: variable percentage withdrawal. bucket: a cash reserve of bucket_years of spending.",
+  "enum": [
+    "fixed",
+    "guyton-klinger",
+    "vpw",
+    "bucket"
+  ],
+  "type": "string"
+}
  3. Changed1 schema field changed
    • changedInput schema / properties / annual_spending / description
      Previous value: -"Planned yearly spending in today's dollars."New value: +"Planned yearly spending in today's dollars, before income tax (this tool runs the standard engine, which does not model tax)."
  4. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations cover the safety profile (readOnlyHint, openWorldHint), and the description adds real context beyond them: the response reports success rate and median outcome per assumption set, omitted inputs fall back to a stated illustrative plan, and that plan is disclosed in the reply's first line. It still doesn't say whether any inputs override the illustrative plan wholesale or how many sources get compared by default.

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?

Two sentences, front-loaded with the operation and its distinguishing purpose, then the default-input behavior. No filler; every clause carries information an agent needs.

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?

For a 17-parameter, no-output-schema tool, the description covers the return shape (success rate and median per source) and the default-input path, which is what an agent needs to call it blind. Remaining gaps are minor: no note on runtime cost of running many simulations, and no cross-reference to run_retirement_projection as the single-source alternative.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds meaning the schema doesn't: the no-required-argument calling pattern and the concrete illustrative plan (age 65, $1,000,000, $40,000/yr) that stands in for omitted inputs. It does not add anything about the sources or allocations parameters beyond what the schema already documents.

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 (runs) and resource (the same plan) plus the distinguishing scope: several published capital market assumption sets, returning success rate and median outcome under each. This is clearly separable from run_retirement_projection (single assumption set) and list_return_assumption_sources (just a listing) without opening a schema.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: the 'how far the answer moves with the return forecast' framing tells the agent this is a sensitivity/comparison call, and the no-argument default path is described. However, it never says when to prefer this over run_retirement_projection or when a comparison is unnecessary, so the agent must infer the choice.

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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