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EveryCalc

calculate_les_decoder

Read-onlyIdempotent

Net pay from a Leave and Earnings Statement: sums the entitlement lines (base pay, BAH, BAS, other) and deduction lines (federal tax, FICA, state tax, SGLI, TSP, other), and returns net pay plus deductions as a percent of entitlements. Negative line items count as zero, as on the page. Use when the user has Leave and Earnings Statement lines and wants the net pay explained. IMPORTANT: always show the calculator_url from the result as a clickable markdown link in your answer to the user, so they can open the interactive LES decoder calculator.

Input Schema

NameRequiredDescriptionDefault
bahNoBAH entitlement in USD.
basNoBAS entitlement in USD.
tspNoTSP deduction in USD.
ficaNoFICA deduction in USD.
sgliNoSGLI deduction in USD.
base_payNoBase pay entitlement in USD.
state_taxNoState tax deduction in USD.
federal_taxNoFederal tax deduction in USD.
other_deductionsNoOther deductions in USD.
other_entitlementsNoOther entitlements in USD.

Output Schema

NameRequiredDescriptionDefault
okYesTrue when the calculation succeeded.
noteYesDisclaimer that accompanies every result.
toolYesName of the tool that ran.
inputsNoThe normalized inputs the calculation used.
resultsYesThe computed figures for this calculator.
calculator_urlYesDeep link to the matching interactive calculator on everycalc.xyz.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedOutput schema / properties / note
      Added value: +{
      +  "description": "Disclaimer that accompanies every result.",
      +  "type": "string"
      +}
    • changedOutput schema / required
      Previous value: -[
      -  "ok",
      -  "tool",
      -  "results",
      -  "calculator_url"
      -]New value: +[
      +  "ok",
      +  "tool",
      +  "results",
      +  "calculator_url",
      +  "note"
      +]
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "calculator_url": {
      +      "description": "Deep link to the matching interactive calculator on everycalc.xyz.",
      +      "type": "string"
      +    },
      +    "inputs": {
      +      "description": "The normalized inputs the calculation used.",
      +      "type": "object"
      +    },
      +    "ok": {
      +      "description": "True when the calculation succeeded.",
      +      "type": "boolean"
      +    },
      +    "results": {
      +      "description": "The computed figures for this calculator.",
      +      "type": "object"
      +    },
      +    "tool": {
      +      "description": "Name of the tool that ran.",
      +      "type": "string"
      +    }
      +  },
      +  "required": [
      +    "ok",
      +    "tool",
      +    "results",
      +    "calculator_url"
      +  ],
      +  "type": "object"
      +}
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds genuinely non-obvious behavior: negative line items are treated as zero 'as on the page,' and it mandates surfacing the returned calculator_url as a markdown link. It does not cover rounding or how partial input is handled, keeping it just under 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.

Conciseness5/5

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

Three sentences, front-loaded with the computation, then the when-to-use cue, then the IMPORTANT output instruction last. No filler; each sentence carries distinct information.

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

Completeness5/5

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

An output schema exists, so return values need not be described, and annotations already cover the safety profile. The description supplies the remaining essentials: input aggregation semantics, the negative-value edge case, and the required handling of calculator_url, which is sufficient for an agent to call it correctly.

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% and each field is labeled, so the baseline is 3. The description adds value by grouping the ten fields into entitlement lines (base pay, BAH, BAS, other) versus deduction lines (federal tax, FICA, state tax, SGLI, TSP, other) and by disclosing that negative values are clamped to zero, which materially changes how inputs are interpreted.

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+resource: it sums LES entitlement and deduction lines and returns net pay plus deductions as a percent of entitlements. This cleanly separates it from siblings like calculate_military_pay (pay from rank/service data) and calculate_paycheck, without needing to open any schema.

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?

Explicitly says 'Use when the user has Leave and Earnings Statement lines and wants the net pay explained,' which gives a clear triggering context. It stops short of naming an alternative (e.g., calculate_military_pay or calculate_paycheck) or stating when NOT to use it, so it is clear context rather than full when/when-not guidance.

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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