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endpoint-aiops-mcp

by AIops-tools
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Python
Remote

patch_status

Assess patch-level distribution across endpoints and list those behind the target patch level, including counts and truncation handling for large fleets.

Instructions

[READ] Patch-level distribution + which endpoints are behind the target level.

Args: target_patch: Desired patch level; omit to use the fleet-majority level. endpoints: Injected inventory rows; skips live collection. target: Endpoint-management target name from config; omit for the default. limit: Max behind-endpoints to return (default 200).

Returns dict: {totalEndpoints, targetPatch, targetSource, distribution, behindCount, behind:{items[], returned, limit, truncated}}. behindCount is the full count; when behind.truncated is true, re-run with a higher limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
targetNo
endpointsNo
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. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It labels the operation [READ], documents the fallback to fleet-majority when target_patch is omitted, and explains truncation semantics and how to re-run with a higher limit. This is transparent about data source behavior and output limits.

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 compact and well-structured into a summary line, Args list, and Returns paragraph. Every sentence carries useful information, and the return-type detail is justified because there is no output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description covers parameter semantics, return dict shape, truncation behavior, and the remedy for truncated results. It omits explicit comparison to patch_compliance, but for a stateless read tool the invocation and output guidance are essentially 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%, but the description's Args section fully compensates by explaining all four parameters: target_patch's defaulting behavior, endpoints as injected inventory that skips live collection, target as a config name with default, and limit as the max behind-endpoints with a default of 200. This adds meaning well beyond the raw schema types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with '[READ] Patch-level distribution + which endpoints are behind the target level,' which names a concrete resource and read action. It clearly states what the tool computes, though it does not explicitly differentiate itself from the sibling patch_compliance.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides parameter-level usage tips, such as omitting target_patch to use the fleet-majority level and injecting endpoints to skip live collection, but it gives no guidance about when to prefer this tool over patch_compliance or other siblings. There is no when-to-use, when-not-to-use, or alternative routing.

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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