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TeamPredict HR Calculators

Full-time equivalent (FTE) and ACA count

fte_calculator
Read-onlyIdempotent

Count full-time equivalent employees (FTE) from full-time staff and groups of part-time staff, and run the Affordable Care Act count that decides whether an employer has 50 or more full-time equivalents. Use it for workforce planning, headcount reporting or checking applicable-large-employer status. FTE is total weekly hours divided by the hours of one full-time schedule; the ACA count uses the IRS rule (30 hours a week is full-time, other employees count their monthly hours up to 120 each, divided by 120). Returns both counts; assumes every month looks like the one entered.

Input Schema

NameRequiredDescriptionDefault
part_time_groupsNoUp to 6 groups of part-time staff, each with a head count and weekly hours per person. Leave empty if there are none.
full_time_employeesYesNumber of full-time employees.
full_time_hours_per_weekNoWeekly hours of one full-time schedule at your company. Default 40.

Output Schema

NameRequiredDescriptionDefault
inputsYesThe inputs as used, with defaults filled in.
resultsYesThe computed numbers. Unavailable numbers are null.
summaryYesOne or two plain sentences with the headline numbers.
calculator_urlYesThe TeamPredict calculator page that shows the same calculation, where a person can change the inputs.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already establish readOnly/idempotent/non-destructive, and the description goes further by disclosing the actual computation rules (weekly hours over one full-time schedule; IRS 30-hour rule with a 120-hour monthly cap) and the modeling assumption that 'every month looks like the one entered'. That assumption is exactly the kind of caveat an agent needs; only rounding/precision and validation failure behavior are left unstated.

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?

Four sentences, front-loaded with the verb and resource, then usage, then method, then return/assumption. No filler and no repetition of schema content.

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 formatting need not be explained; the description still notes that both counts are returned. Combined with the disclosed formulas and the month-uniformity assumption, an agent has everything needed to invoke this 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%, so the baseline is 3, but the description earns credit above that by explaining the role of full_time_hours_per_week as the FTE divisor and how part-time groups feed the ACA 120-hour cap. It adds conceptual meaning rather than merely restating field names.

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 and resource ('Count full-time equivalent employees') plus a second concrete output (ACA count) and the regulatory threshold it decides. Nothing in the sibling list overlaps with this domain, so an agent can route here without ambiguity.

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?

Names three concrete use cases ('workforce planning, headcount reporting or checking applicable-large-employer status'), which gives clear when-to-use context. It stops short of stating exclusions or naming a sibling alternative for adjacent questions (e.g., hourly_to_salary or overtime_pay).

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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