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fabric-aiops

uplink_loss_and_latency_rca

Rank MX WAN uplinks by loss and latency, identify the worst performers, and get a likely cause plus recommended action.

Instructions

[READ] Rank the worst MX WAN uplinks by loss + latency, map cause + action.

The flagship uplink RCA: pulls per-uplink loss/latency across the org (MX
security appliances), ranks the worst uplinks by a composite of average loss
and latency, flags each degraded uplink against the thresholds, and attaches
a likely cause and a recommended action. Every ranking carries its numbers,
not a black-box verdict. Pass 'records' for pure analysis, or an org/target
to pull live.

Args:
    loss_pct: Avg loss %% at/above which an uplink is degraded (default 5.0).
    latency_ms: Avg latency ms at/above which an uplink is degraded (default 150).
    records: Injected uplink series — {serial, networkId, uplink, ip,
        timeSeries:[{lossPercent, latencyMs}]}; skips live collection.
    org_id: Meraki organization id for live pull; omit to use target default.
    limit: Max rows in the ranked list (default 100). The result carries
        'returned'/'limit'/'truncated'; re-run with a higher limit when
        'truncated' is true rather than treating the list as complete.
    target: Target name from config; omit for the default.

Returns dict: {uplinksEvaluated, degradedCount, thresholds, worst:[{serial,
    networkId, uplink, ip, avgLossPct, maxLossPct, avgLatencyMs, maxLatencyMs,
    degraded, cause, action}], returned, limit, truncated, note}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
org_idNo
targetNo
recordsNo
loss_pctNo
latency_msNo
Behavior5/5

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

No annotations, but description fully discloses read operation, ranking criteria, thresholding, and non-black-box verdict. Also warns about truncation.

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?

Well-structured with summary and Args section. Slightly lengthy but each sentence adds value.

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?

Covers all necessary input and output details, including return dict fields, despite no output schema.

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?

Despite 0% schema coverage, description documents every parameter with defaults and explanation, including truncation behavior for limit.

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 it ranks worst MX WAN uplinks by loss+latency, maps cause and action. It distinguishes from siblings like device_uplinks by adding RCA analysis.

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?

Provides context for when to use: for live pull or records injection, with a note on truncation. Does not explicitly exclude alternatives but context is clear.

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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