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DevOps Dashboard MCP Server

DevOps Dashboard MCP Server

by turlockmike
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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system_health

Run an aggregate health check across CPU, memory, disk, services, and ports. Returns clear healthy or issues-detected status with specific warnings and thresholds.

Instructions

Run an aggregate health check across the entire system. Checks load averages, memory/swap usage, disk space (with inode check), failed systemd services, and listening port count. Returns clear HEALTHY/ISSUES DETECTED status with specific warnings and thresholds. This is the go-to tool for a quick 'is everything OK?' check.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "system_health"
  }
}

Input Schema

NameRequiredDescriptionDefault
verboseNoIf true, include full details for each subsystem instead of a summary.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden and does so well: it lists the subsystems checked and the return format (HEALTHY/ISSUES DETECTED with specific warnings and thresholds). It does not explicitly state that the operation is read-only and makes no changes, but a health check strongly implies that.

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?

Three sentences, each earning its place: purpose, checked subsystems, and output behavior. The final sentence reinforces the primary use case without redundancy. Information is front-loaded and skimmable.

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?

For an aggregate health tool with no output schema and no annotations, the description gives enough context for an agent to decide and call it: inputs, scope, status format, and use case. It stops short of listing the actual thresholds, but explicitly states that thresholds and warnings exist, which is sufficient for a 'quick check' tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the only parameter, verbose, already has a clear description in the schema. The tool description adds context about the overall report but no additional meaning for the parameter itself, so the baseline of 3 is appropriate.

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 states a specific action ('Run an aggregate health check'), a clear resource ('entire system'), and enumerates exactly what it checks: load averages, memory/swap, disk space with inode check, failed systemd services, and listening port count. It distinguishes itself from subsystem-specific siblings by clearly positioning itself as an aggregate check.

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 explicitly frames this as the 'go-to tool for a quick is everything OK? check,' which tells an agent when to select it. It does not explicitly say when to prefer the sibling tools for deeper subsystem investigation, but the contrast with the sibling names is implied.

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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