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industrial-aiops

OT-AIops Energy

subscription_health

Read-only

Analyze subscription feed sequence numbers to detect dropped, duplicate, or out-of-order notifications and flag overloaded channels, returning a verdict and recommendation.

Instructions

[READ][risk=low] Health of a sequenced subscription feed (OPC-UA or Sparkplug B).

Detects dropped notifications (sequence gaps), duplicates / out-of-order, a high
republish-rejection rate, and overloaded channels — the classic Kepware
"too many tags on one channel → republish/queue-flush dropouts" fault.

Args:
    sequence: Sequence numbers actually received, in arrival order.
    republish_requested: How many republish requests were made.
    republish_rejected: How many were rejected (server couldn't keep up).
    tags_per_channel: {channel/endpoint: tag_count} — flags channels over the max.
    max_tags_per_channel: Density above which a channel is flagged (default 5000).
    wrap_at: Modulus for rolling counters (e.g. 256 for Sparkplug B seq); omit
        for monotonic OPC-UA counters.

Returns dict: {received, missed_count, duplicate_count, out_of_order_count,
    republish_requested, republish_rejected, republish_reject_rate,
    overloaded_channels:[{channel, tags}], max_tags_per_channel,
    verdict ('ok'|'reordered'|'lossy'|'overloaded'), recommendation}.

Example: subscription_health(sequence=[1,2,4,5], tags_per_channel={"ch1":7000}).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wrap_atNo
sequenceYes
tags_per_channelNo
republish_rejectedNo
republish_requestedNo
max_tags_per_channelNo
Behavior5/5

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

Beyond the readOnlyHint, the description discloses detailed behavior: detection categories, meaning of parameters (e.g., 'server couldn't keep up' for republish_rejected), the return dict structure, and a concrete example. This goes well beyond the annotation basics.

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: starts with a READ/risk tag, then a terse summary, followed by detection targets, Args, Returns, and an Example. Every sentence contributes information, with no 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?

With no output schema, the description fully specifies the return dictionary and verdict values. All 6 parameters are documented, an example is given, and the classic failure scenario is described. This is sufficient for an agent to select and invoke the tool 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?

Schema coverage is 0%, but the description's Args section explains every parameter's meaning, types, defaults, and usage (e.g., 'wrap_at: Modulus for rolling counters... omit for monotonic OPC-UA counters'). This adds essential semantics 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 clearly states the tool's purpose: 'Health of a sequenced subscription feed' and explicitly lists what it detects (dropped notifications, duplicates, out-of-order, republish rejections, overloaded channels). This specific verb+resource combination distinguishes it from sibling health tools like tag_health or heartbeat_health.

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?

It provides clear context on when to use: for diagnosing subscription feed health issues, with a concrete 'classic Kepware' failure scenario. However, it doesn't explicitly name alternative tools or state when *not* to use it, so it falls short of full 5.

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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