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industrial-aiops

OT-AIops Energy

oee_compute

Read-only

Compute OEE from production inputs, identify Six Big Losses, and assess energy usage vs baseline when energy data is supplied.

Instructions

[READ][risk=low] OEE = Availability × Performance × Quality (+ loss/energy depth).

Args:
    planned_time_s: Planned production time (seconds).
    run_time_s: Actual running time (seconds) — planned minus downtime.
    ideal_cycle_time_s: Ideal/nameplate cycle time per part (seconds).
    total_count: Total parts produced.
    good_count: Good (non-reject) parts produced.
    breakdown_time_s: Optional — unplanned-stop seconds (splits availability loss).
    setup_time_s: Optional — changeover/setup seconds (splits availability loss).
    minor_stop_time_s: Optional — minor-stop seconds (splits performance loss;
        the remainder is speed loss).
    startup_reject_count: Optional — startup/warm-up rejects (splits quality
        loss; the remainder is production rejects).
    actual_kwh: Optional — measured energy for this run; enables the energy block.
    baseline_kwh: Optional — expected/baseline energy for the actual-vs-baseline
        deviation verdict.
    emission_factor_kg_per_kwh: Optional — carbon factor (kg CO2e/kWh). Default is
        a flagged placeholder (see the tool's carbon note); pass the grid's value.
    energy_tolerance: ± band (fraction) for the over/under/on-target verdict.

Returns dict: OEE factors + oee/oee_pct + inputs + losses, plus
    ``six_big_losses`` (breakdown/setup/minor-stops/speed/startup/production-reject
    time-ladder that sums with OEE to 100%) and, when ``actual_kwh`` is given,
    ``energy`` (kwh_per_unit, carbon, and baseline deviation).

Example: oee_compute(planned_time_s=28800, run_time_s=25200,
    ideal_cycle_time_s=2.0, total_count=12000, good_count=11800,
    setup_time_s=1800, actual_kwh=940, baseline_kwh=880).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actual_kwhNo
good_countYes
run_time_sYes
total_countYes
baseline_kwhNo
setup_time_sNo
planned_time_sYes
breakdown_time_sNo
energy_toleranceNo
minor_stop_time_sNo
ideal_cycle_time_sYes
startup_reject_countNo
emission_factor_kg_per_kwhNo
Behavior5/5

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

Annotations already mark the tool as read-only and non-destructive, and the description reinforces this with [READ][risk=low]. It adds substantial behavior detail: optional parameters split loss categories, energy block activates only when actual_kwh is provided, and emission factor default is a flagged placeholder. No contradictions with annotations.

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?

The description is well-structured with a formula, Args block, Returns block, and Example. It is longer than average, but each parameter definition and return detail earns its place given the tool's 13 parameters. The information is front-loaded and organized.

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?

With no output schema and a complex computation, the description is comprehensive: it covers all inputs, output keys, loss breakdown, energy/carbon behavior, and provides a representative example. It leaves little ambiguity for an agent selecting and invoking the tool.

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

Parameters5/5

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

The input schema has 0% description coverage, so the description carries full responsibility. It defines every parameter with units and semantics, explains how optional parameters split losses, and includes a worked example. This fully compensates for the schema's lack of detail.

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?

The description clearly states the tool computes OEE using the formula Availability × Performance × Quality and describes the returned dictionary. However, it does not distinguish this from the sibling tool oee_multidim, so it lacks sibling differentiation.

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?

There is no explicit guidance on when to use this tool versus alternatives. The description reads like a function specification with parameters and returns, but it does not mention oee_multidim or any conditions for choosing this tool over others.

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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