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endpoint-aiops-mcp

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Python
Remote

drift_report

Identify endpoints that have drifted from the fleet baseline (patch, agent, OS, profile), deriving the baseline from fleet majority when none is set.

Instructions

[READ] Report endpoints drifted from a per-field baseline (patch/agent/OS/profile).

Answers "which endpoints have drifted from the fleet?" With no baseline it derives one by fleet majority (the most common value per field is treated as intended), so it works before any gold image is declared. Pass 'endpoints' for pure analysis, or a target to pull live inventory.

Args: baseline: Field→intended-value map; omit to derive the fleet majority. fields: Inventory fields to check (default agentVersion, patchLevel, osBuild, profileId). endpoints: Injected inventory rows; skips live collection. target: Endpoint-management target name from config; omit for the default. limit: Max drifted endpoints to return (default 200).

Returns dict: {totalEndpoints, baselineSource, baseline, fieldsChecked, driftedCount, compliantCount, driftByField, driftedEndpoints:{items[], returned, limit, truncated}}. driftedCount is the full count; when driftedEndpoints.truncated is true, re-run with a higher limit.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
fieldsNo
targetNo
baselineNo
endpointsNo

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. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses the read-only nature with '[READ]', explains the baseline derivation behavior when no baseline is provided (fleet majority), and details truncation semantics with the 'truncated' flag and guidance to re-run with a higher limit. This is thorough behavioral disclosure.

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 well-structured with a clear lead-in, a succinct answer to the core question, and a bulleted argument list. It includes a concise example. Every sentence adds value; there is no fluff. It is appropriately sized for the tool's complexity.

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?

The description provides the return dict structure, explains the meaning of 'driftedCount' vs 'truncated', and gives a concrete example. It covers all necessary details for an agent to invoke the tool correctly, including the optional baseline derivation and the endpoints/target alternatives. Nothing essential is missing.

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 compensate. It does so comprehensively: each parameter (baseline, fields, endpoints, target, limit) is explained with its purpose and default behavior, going beyond the schema's type/default information. For example, it clarifies that omitting baseline derives fleet majority and that endpoints skips live collection.

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 the tool's function: 'Report endpoints drifted from a per-field baseline.' It uses a specific verb and resource, and it distinguishes itself from sibling tools like overview or patch_compliance by focusing on drift analysis. The '[READ]' prefix further clarifies its nature.

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 explains when to use it: 'Answers which endpoints have drifted from the fleet?' and provides guidance on using 'endpoints' for pure analysis vs 'target' for live inventory. It doesn't explicitly name alternative tools or exclusion conditions, but the context is clear enough for an agent to decide when to invoke it.

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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