Skip to main content
Glama
Sign In
endpoint-aiops-mcp

endpoint-aiops-mcp

by AIops-tools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

endpoint_health_score

Read-only

Scores each managed endpoint from 0-100, ranks worst first, and cites reasons such as offline, stale, patch-behind, or agent-behind to show which endpoints need attention.

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

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?

Annotations only declare readOnlyHint=true; the description adds substantial behavioral context beyond that — the deduction model and cited 'reasons', the fact that no live pull occurs, the fleet-majority baseline derivation when no baseline is supplied, and the truncation/re-run semantics. This is exactly the extra context annotations cannot carry.

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?

Front-loaded with the one-line purpose, then progressively deeper detail in Args/Returns/Example blocks. The length is justified by zero schema coverage and no output schema — each section supplies information the structured fields lack. No filler sentences.

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 documents the return dict shape (endpointsEvaluated, baseline, summary bands, worst.items, truncated, note) and explains the truncation workflow. Combined with the example, an agent has everything needed to call and interpret the tool.

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 carry the load, and it does: it enumerates the expected endpoint row fields (id, hostname, online, lastSeenHours, agentVersion, patchLevel), defines stale_hours semantics and default, explains baseline omission behavior, and defines limit's worst-first truncation. All four parameters are meaningfully specified.

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?

Opens with a specific verb+resource+output shape: 'Composite per-endpoint health score (0-100), worst endpoints first.' This is clearly distinguishable from siblings like patch_status, drift_report, and overview, which cover narrower or different scopes. An agent knows exactly what it gets back.

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?

Explicitly frames the use case ('which endpoints are worst?') and states a critical usage constraint: it is pure analysis over injected inventory rows shaped like endpoint_list output, with no live connection. It doesn't explicitly name an alternative sibling (e.g., overview vs drift_report) for adjacent questions, which keeps it just short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools