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industrial-aiops

OT-AIops Energy

tag_health

Read-only

Evaluate OT tag samples to detect bad quality, flatlines, out-of-range values, and anomalies. Rank offenders by severity to prioritize remediation.

Instructions

[READ][risk=low] Rank tag offenders by bad-quality / flatline / range / anomaly.

Args:
    tags: Per-tag dicts — {ref, label?, samples:[scalars or {value, good|quality}],
        warn_high?, alarm_high?, warn_low?, alarm_low?}.
    thresholds: Optional {ref: {warn_high, alarm_high, warn_low, alarm_low}} override.

Returns dict: {evaluated, overall ('ok'|'warn'|'alarm'), offender_count,
    offenders:[{ref, label, samples, latest, flags:[...], anomaly_count,
    severity (0..3)}], results:[...]}. Flags include bad_quality, flatline,
    out_of_range_warn/alarm, statistical_anomaly.

Example: tag_health(tags=[{"ref":"ns=2;i=5","samples":[70,71,70,99]}]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsYes
thresholdsNo
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds a '[READ][risk=low]' preface consistent with that. It also discloses the exact output structure, severity levels, and flag types, providing meaningful behavioral detail beyond the 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: a compact summary line, clear Args/Returns sections, and a short example. Every sentence adds valuable detail without redundancy or filler.

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?

Despite having no output schema, the description fully explains the return dictionary, flags, severity range, and includes a realistic example. The complexity is moderate and the description covers parameters, behavior, and results completely.

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?

Schema coverage is 0%, but the description fully compensates by documenting the 'tags' structure (fields like ref, label, samples, thresholds) and explaining the optional 'thresholds' override. It also provides an example call, making parameter usage unambiguous.

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?

The description opens with a specific verb and resource: 'Rank tag offenders by bad-quality / flatline / range / anomaly.' This clearly states what the tool does and distinguishes it from generic health or scan tools by focusing on per-tag offender ranking with concrete detection categories.

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?

The description implies usage context by defining the tool's role as ranking tag offenders, but it does not explicitly state when to prefer this tool over siblings like anomaly_scan or health_summary. No when-not-to-use conditions or alternatives are mentioned.

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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