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industrial-aiops

OT-AIops Energy

downtime_root_cause_live

Read-only

Automatically diagnose downtime root causes by gathering live evidence from your endpoint—sampling tags, checking dataflow, and retrieving active alarms during the incident window.

Instructions

[READ][risk=low] AI downtime RCA copilot that GATHERS its own live evidence.

Same advisory, read-only, evidence-cited contract as downtime_root_cause — but
instead of hand-injecting evidence you give an endpoint + incident window and it
pulls the evidence itself: a cross-protocol diagnose_dataflow probe, a short
sampled series per ref (so flatline/bad-quality/anomaly surface via tag_health),
and active OPC-UA conditions. Light read load; non-destructive; nothing executed.
The gathered bundle is echoed under 'collected_evidence' (no hidden inputs).

Args:
    endpoint: Endpoint name from config (any protocol). Omit for the default.
    window: {start (ISO-8601), end?, asset?, category?, freshness_threshold_s?}.
    refs: Tags/nodes/addresses to sample for this incident (first is also the
        diagnose_dataflow target). Capped at 20.
    sample_count: Reads per ref to build its series (1..60, default 8).
    interval_ms: Delay between reads (>=50ms, default 200).
    include_alarms: Surface active OPC-UA conditions as alarm evidence (OPC-UA only).
    lead_window_s: Causal lead window before onset (default 300s).
    include_graph: When true, also return the 'graph' block (same {nodes, edges,
        mermaid, meta} causal-graph re-projection as downtime_root_cause). Pure
        re-shape of the verdict; no new reasoning. Omit for the flat verdict.

Returns dict: same shape as downtime_root_cause plus 'collected_evidence'
    {endpoint, protocol, refs_sampled, alarms_found, dataflow_verdict}.

Example: downtime_root_cause_live(endpoint="line1",
    window={"start":"2026-06-28T10:00:00Z","asset":"line1"},
    refs=["ns=2;i=5","ns=2;i=6"]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refsNo
windowNo
endpointNo
interval_msNo
sample_countNo
include_graphNo
lead_window_sNo
include_alarmsNo
Behavior5/5

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

Beyond readOnlyHint=true and destructiveHint=false, the description discloses live evidence gathering, sampling caps (refs capped at 20), load controls (sample_count, interval_ms), graph as 'pure re-shape', and 'no hidden inputs'. It also confirms non-destructive and nothing executed, enriching the annotation profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy but well-structured with distinct sections: purpose, Args, return shape, and example. Every sentence carries information, but the length is justified given the 8 parameters and behavioral nuance. Could be slightly trimmed without loss.

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?

Given the tool's complexity and absence of output schema, the description fully covers inputs, behavior, return shape (referencing downtime_root_cause plus 'collected_evidence'), and provides a concrete example. It also notes the graph block's structure. No significant gaps.

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?

With 0% schema coverage, the description compensates fully by explaining every parameter's meaning, type, constraints, defaults, and relationships (e.g., 'first is also the diagnose_dataflow target', 'OPC-UA only'). This far exceeds the bare schema and gives precise operational guidance.

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 the tool is an 'AI downtime RCA copilot that GATHERS its own live evidence' with a specific verb (gathers) and resource (live evidence). It explicitly distinguishes from sibling downtime_root_cause: 'instead of hand-injecting evidence you give an endpoint + incident window and it pulls the evidence itself'.

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 clearly differentiates when to use this tool vs downtime_root_cause by explaining the difference in evidence handling (auto-gathered vs hand-injected). It also notes 'Light read load; non-destructive; nothing executed', which signals safe contexts, but does not explicitly list 'when not to use' exclusions.

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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