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industrial-aiops

OT-AIops Energy

health_summary

Read-only

Classifies OPC-UA tags against warn/alarm thresholds. Returns counts of ok/warn/alarm/unknown and lists offending tags.

Instructions

[DEPRECATED → opcua_health_summary][READ][risk=low] Classify OPC-UA tags.

Classifies tag node-ids against warn/alarm thresholds. Returns
ok/warn/alarm/unknown counts plus the offending tags. Thresholds
come from config tags, or per-ref overrides in ``thresholds``.

Args:
    endpoint: Endpoint name from config.
    node_ids: Tag node ids to evaluate; omit to use configured tags.
    thresholds: Optional {ref: {warn_high, alarm_high, warn_low, alarm_low}}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endpointNo
node_idsNo
thresholdsNo
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: the tool is deprecated and redirected to opcua_health_summary, thresholds come from config or per-ref overrides, and the return includes ok/warn/alarm/unknown counts plus offending tags. This goes beyond annotations without contradicting them.

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 compact and front-loaded with the deprecation warning and READ status. The two-sentence summary plus a concise Args block provides necessary information without waste. Every sentence serves a purpose: deprecation, safety, behavior, and parameter meaning.

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, the description adequately explains the return value (counts and offending tags). It also covers default behavior (configured tags), override mechanism (thresholds), and deprecation. For a 3-parameter read-only tool, this is sufficient for an agent to select and invoke it correctly.

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 only provides types and defaults with no descriptions, so the description carries full weight. The Args section clearly explains each parameter: endpoint as a config name, node_ids as tag node IDs with an omit-to-use-configured-tags behavior, and thresholds as an optional dict of per-ref overrides. This adds essential meaning the schema lacks.

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 states a specific verb ('Classify') and resource ('OPC-UA tags') with clear scope: 'Classifies tag node-ids against warn/alarm thresholds.' It distinguishes itself from generic health tools by specifying the OPC-UA context, threshold-based classification, and return contents (counts plus offending tags).

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?

The description provides clear context on how to use the tool: it explains that node_ids can be omitted to use configured tags, and that thresholds can be overridden. It does not explicitly state when to use this vs. sibling tools like tag_health or subscription_health, but the OPC-UA and threshold-specific language implies the intended scope.

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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