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Python
Remote

endpoint_health_score

Rank endpoints by health score (0-100) to surface the worst offenders first. Deduct points for offline, stale, patch-behind, and agent-behind signals, with cited reasons.

Instructions

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

Answers "which endpoints are worst?" by folding the fleet signals into one ranked view: it deducts points for each risk signal (offline, stale, patch-behind, agent-behind) and cites every deduction in the endpoint's 'reasons'. Pure analysis over injected inventory rows (the shape from endpoint_list) — no live connection is used. With no baseline, the patch and agent baselines are derived by fleet majority, so it works before a gold image is declared.

Args: endpoints: Injected inventory rows (id, hostname, online, lastSeenHours, agentVersion, patchLevel) to score. Required — no live pull. stale_hours: An endpoint whose lastSeenHours >= this is 'stale' (default 24). baseline: Intended {'agentVersion', 'patchLevel'}; omit to derive the fleet majority. limit: Max worst-first endpoints to return (default 50).

Returns dict: {endpointsEvaluated, baseline:{agentVersion, patchLevel, source}, summary:{healthy, degraded, critical}, worst:{items:[{endpoint, score, band, reasons[]} ...worst-first], returned, limit, truncated}, note}. The summary always counts the whole fleet; when worst.truncated is true, re-run with a higher limit to see further down the ranking.

Example: endpoint_health_score(endpoints=[ {"hostname":"tc01","online":True,"agentVersion":"12.6","patchLevel":"2026-06"}, {"hostname":"tc02","online":False,"agentVersion":"12.5","patchLevel":"2026-05"}]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
baselineNo
endpointsYes
stale_hoursNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.10.3
    • addedInput schema / properties / limit
      Added value: +{
+  "default": 50,
+  "title": "Limit",
+  "type": "integer"
+}
  2. Addedv0.2.0

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses that the tool is read-only, uses no live connection, deducts points for specific risk signals, cites deductions in 'reasons', derives baselines by fleet majority when none is given, and describes truncation behavior. This is exceptionally transparent for an unannotated tool.

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 structured with a summary, Args, Returns, and an Example. Every section adds necessary information: scoring logic, parameter semantics, output shape, and truncation guidance. There is minor redundancy about 'no live connection', but overall it is dense and purposeful rather than padded.

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?

For a 4-parameter tool with no output schema and no annotations, the description is remarkably complete. It covers all parameter meanings, the scoring model, baseline derivation, return structure, and a practical edge case (truncation). The presence of an output shape description compensates fully for the missing 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?

Schema description coverage is 0%, so the description must fully document parameters. It does: 'endpoints' is described with its expected shape, 'stale_hours' has a threshold definition and default, 'baseline' is explained with its intended object shape and fallback, and 'limit' has a default and behavioral meaning. The example further clarifies real usage.

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 opens with '[READ] Composite per-endpoint health score (0-100), worst endpoints first' and explicitly answers 'which endpoints are worst?'. This is a specific verb, resource, and ranking behavior. It also distinguishes itself from siblings by stating it analyzes injected inventory rows rather than using a live connection.

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 states when to use it: when you want a ranked health score over already-injected endpoint inventory rows, and it notes no live connection is used. It also explains baseline fallback behavior. It does not explicitly name alternative tools or say when not to use it, but the context is strong enough to guide selection.

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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