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Lumino

by geored
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ci_cd_performance_baselining_tool

Establish performance baselines for CI/CD pipelines and detect deviations from historical norms using Prometheus metrics and Kubernetes data.

Instructions

Establish performance baselines for pipelines and flag runs deviating from historical norms.

Uses Prometheus metrics from Tekton controller for accurate historical performance data.
Falls back to Kubernetes API if Prometheus is unavailable.

Args:
    pipeline_names: Pipelines to analyze (default: all).
    baseline_period: "7d", "30d" (default), or "90d".
    deviation_threshold: Std deviations to trigger alerts (default: 2.0).
    performance_metrics: Metrics: "duration", "cpu", "memory", "success_rate" (default: all).
    update_frequency: "daily" (default) or "weekly".
    include_task_level: Include task-level analysis (default: True).

Returns:
    Dict: Baselines, recent runs analysis, trends, and optimization opportunities.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pipeline_namesNo
baseline_periodNo30d
deviation_thresholdNo
performance_metricsNo
update_frequencyNodaily
include_task_levelNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it explains the data sources (Prometheus with Kubernetes fallback), describes the alerting mechanism ('flag runs deviating'), and mentions the return structure. However, it doesn't cover rate limits, authentication needs, or potential side effects.

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 efficiently structured with a purpose statement, behavioral context, parameter documentation, and return format - all in appropriate sections. Every sentence earns its place, and the information is front-loaded with the core purpose stated first.

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?

Given the tool's complexity (6 parameters, no annotations) and the presence of an output schema, the description is complete enough. It covers purpose, behavior, all parameters with semantics, and mentions the return structure, while the output schema handles return value details.

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?

With 0% schema description coverage, the description fully compensates by providing comprehensive parameter documentation in the 'Args' section. Each of the 6 parameters is clearly explained with acceptable values, defaults, and semantics, adding significant value beyond the bare schema.

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 purpose with specific verbs ('establish performance baselines', 'flag runs deviating') and resources ('pipelines', 'historical norms'). It distinguishes itself from sibling tools by focusing on performance baselining rather than investigation, analysis, or monitoring functions.

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

Usage Guidelines3/5

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

The description implies usage context through its data source explanation ('Uses Prometheus metrics... Falls back to Kubernetes API'), but doesn't explicitly state when to use this tool versus alternatives like 'analyze_failed_pipeline' or 'detect_anomalies'. No explicit when-not-to-use guidance or named alternatives are provided.

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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