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fabric-aiops

network_health_score

Calculate a composite fleet health score (0-100) per network by combining device online percentage, uplink health, and alert severity penalty. Ranks networks worst-first with explainable components.

Instructions

[READ] Composite fleet health score per network (0-100), worst-first.

Folds device online %%, uplink health %%, and an alert-severity penalty into
one weighted score per network (0.5 / 0.3 / 0.2), every component returned so
the number is explainable. Pure analysis over injected rows — no live pull.

Args:
    device_statuses: rows {serial, networkId, status, productType} (e.g. from
        org_device_statuses' 'devices').
    uplinks: optional rows {networkId, status} (active/ready = healthy).
    alerts: optional rows {networkId, severity} (critical/warning/info).
    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.

Returns dict: {networksEvaluated, fleetScore, summary:{healthy, degraded,
    critical}, weights, worst:[{networkId, score, band, devicesOnline,
    devicesTotal, onlinePct, uplinkHealthPct, alertPenalty}], returned,
    limit, truncated, note}.

Example: network_health_score(device_statuses=[
    {"networkId":"N1","status":"online"},
    {"networkId":"N1","status":"offline"}]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
alertsNo
uplinksNo
device_statusesYes
Behavior4/5

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

No annotations provided; the description carries the burden. It discloses it is a read-only analysis over injected data, includes a '[READ]' tag, and explains the scoring components. Missing details on authentication or error handling, but sufficient for a non-destructive operation.

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?

Well-structured with a clear title, breakdown of the score, Args, Returns, and an example. Every sentence adds value; no fluff. Front-loaded with the essential purpose.

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 no output schema, the description lists all return fields, including behavior of limit and truncation. Coverable all necessary contextual information for an agent to use 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?

The description includes a detailed 'Args:' section covering all four parameters with expected structure and defaults, fully compensating for the 0% schema description coverage. Adds meaning beyond type-only schema.

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 computes a composite fleet health score per network (0-100) from device, uplink, and alert data. It distinguishes from sibling tools by focusing on a weighted score rather than raw data retrieval.

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?

Provides context on how to use the tool (injecting rows, limit behavior) but does not explicitly compare to sibling tools or state when not to use it. Implicit guidance from 'no live pull' but lacks clear exclusion criteria.

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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