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endpoint-aiops-mcp

endpoint-aiops-mcp

by AIops-tools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

patch_compliance

Read-only

Measure the percentage of endpoints on a target patch level, check whether fleet compliance meets an SLA threshold, and list non-compliant hosts.

Instructions

[READ] Patch-compliance SLA measure: % of the fleet on the target patch level.

Reframes patch_status (the patch-level distribution) as an SLA/compliance verdict: what fraction of the fleet is on the target level, whether that meets the SLA, and which endpoints are non-compliant. Compliance is an exact string match on patchLevel — a transparent check, not a version-semantics parser. Injected-only: pass 'endpoints' (inventory rows); no live collection.

Args: endpoints: Injected inventory rows to evaluate (e.g. from endpoint_list). target_patch: Desired patch level; omit to use the fleet-majority level. sla_pct: Compliance SLA threshold percent; default 95.0. limit: Max non-compliant endpoints to return (default 200).

Returns dict: {endpointsEvaluated, targetPatch, targetSource, slaTargetPct, complianceRatePct, compliantCount, verdict, nonCompliantCount, nonCompliant:{items[], returned, limit, truncated}, note}. The rate is always computed over the whole fleet, never over the capped list.

Example: patch_compliance(endpoints=[{"hostname":"tc01","patchLevel":"2026-06"}, {"hostname":"tc02","patchLevel":"2026-05"}], target_patch="2026-06").

Input Schema

NameRequiredDescriptionDefault
limitNo
sla_pctNo
endpointsYes
target_patchNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.10.3
    • addedInput schema / properties / limit
      Added value: +{
+  "default": 200,
+  "title": "Limit",
+  "type": "integer"
+}
  2. Addedv0.2.0

TDQS

A4.7/5.0
Behavior5/5

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

With readOnlyHint already covering safety, the description adds substantial behavior: injected-only operation requiring caller-supplied inventory, exact string-match semantics on patchLevel ('not a version-semantics parser'), and the truncation invariant that the rate is always over the whole fleet, never the capped list. These are non-obvious traits no annotation conveys.

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?

Front-loaded with the core verdict and the distinguishing contrast first, then Args, Returns, and an Example. Every section is useful, though enumerating the full return dict key list is somewhat list-y and could be trimmed without losing meaning.

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?

No output schema exists, and the description compensates by documenting the return dict keys and the semantics of the rate computation, plus a concrete example. For a 4-param, no-output-schema tool this is fully complete.

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 full load, and it does: endpoints (injected inventory rows, e.g. from endpoint_list), target_patch (omit to use fleet-majority), sla_pct (default 95.0), and limit (suggested default 200). Each parameter gains meaning beyond its bare title/default.

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?

States a specific verb+resource ('Patch-compliance SLA measure: % of the fleet on the target patch level') and explicitly distinguishes itself from its sibling by naming patch_status and explaining the reframing. An agent can tell it apart from patch_status without opening either schema.

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?

Clearly routes the agent: it explains this is a compliance-verdict view of patch_status and that the operation is injected-only ('pass endpoints ... no live collection'), pointing to endpoint_list as the source. It gives strong context but stops short of an explicit when-not-to-use rule (e.g. when to prefer raw patch_status).

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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