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chexma
by chexma

vibemk_get_service_metrics

Retrieve service performance metrics from CheckMK for a given host and service, with options for metric, time range, and aggregation.

Instructions

📊 Get service metrics - Extract service performance data from CheckMK metrics API

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteNoCheckMK site namecmk
reduceNoAggregation function: 'max', 'min', 'average'max
host_nameYesHost name
time_rangeNoTime range: '1h', '4h', '24h', '7d', '30d'1h
metric_nameNoSpecific metric ID (e.g., 'if_in_bps', 'cpu_util')
service_descriptionYesService description

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.3.10

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'Extract service performance data from CheckMK metrics API' — this implies a read operation but doesn't disclose auth requirements, aggregation behavior beyond the schema defaults, return format, pagination, or failure modes. For an unannotated tool this is a significant gap.

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?

One efficient sentence that front-loads the purpose with an emoji affordance. There's no fluff, but it's so thin it borders on under-specification rather than disciplined conciseness — the brevity is good, yet it sacrifices the behavioral and usage content other dimensions needed.

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

Completeness2/5

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

With no output schema and no annotations, the description must explain what a caller gets back (metric values, timestamps, units) and how the metrics API behaves. It does neither. For a metrics-extraction tool of moderate complexity, this is incomplete — an agent cannot anticipate the return shape or edge cases.

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 every parameter (site, reduce, host_name, time_range, metric_name, service_description) already has a meaningful description in the schema. The description adds nothing about parameters, so the baseline 3 applies — the schema does the heavy lifting correctly.

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 uses a specific verb-resource pair ('Get service metrics' / 'Extract service performance data') and names the data source (CheckMK metrics API). It's clear what the tool does, but it doesn't explicitly differentiate from close siblings like vibemk_get_host_metrics, vibemk_get_service_status, or vibemk_search_metrics — the resource distinction ('service' vs 'host') is implied by the name but never stated.

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?

No guidance on when to use this tool vs alternatives. Given several overlapping siblings (vibemk_get_host_metrics, vibemk_get_service_status, vibemk_search_metrics, vibemk_get_custom_graph), an agent gets no help choosing among them. There are no when/when-not statements or alternative tool references.

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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