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AIops-tools

cicd-aiops

pipeline_failure_rca

Classify failed pipelines to diagnose root causes and prescribe actionable fixes, with evidence for each classification.

Instructions

[READ] Classify recent failed pipelines: cause + action per pipeline.

The flagship pipeline RCA: pulls the project's recent failed pipelines with their failed jobs and trace tails, classifies each failed job (test-failure / dependency-network / runner-timeout / oom / script-error) from its failure_reason and trace markers, and attaches a cause and a recommended action. Every classification names its matched evidence, not a black-box verdict. Pass 'failed_pipelines' for pure analysis, or a project to pull live.

Args: project: Project id or full path (required unless failed_pipelines given). limit: How many recent failed pipelines to pull (default 10). tail_lines: Trace-tail lines pulled per failed job (default 60). failed_pipelines: Injected rows {id, ref, jobs:[{name, stage, status, failureReason, traceTail}]}; skips the live pull. target: Server target name from config; omit for the default.

Returns dict: {pipelinesEvaluated, classCounts, pipelines:[{pipeline, ref, headlineClass, cause, action, failedJobs:[{job, stage, class, cause, action, evidence}]}], note}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
targetNo
projectNo
tail_linesNo
failed_pipelinesNo
Behavior4/5

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

The description opens with '[READ]', indicating a non-destructive operation. It details that it pulls live data and classifies failures with evidence, but does not mention required permissions or side effects. Given no annotations, this is adequate.

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?

The description is somewhat lengthy but well-structured with a concise intro, a detailed functional paragraph, bulleted Args, and return spec. It front-loads the verb and purpose, avoiding unnecessary repetition.

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?

Despite no output schema, the description details the full return dict structure (pipelinesEvaluated, classCounts, pipelines with nested failedJobs, etc.) and explains classification categories and evidence. This makes it complete for the tool's complexity.

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 explains all 5 parameters (project, limit, tail_lines, failed_pipelines, target) with their types, defaults, and relationships (e.g., 'required unless failed_pipelines given'). This comprehensively compensates for the schema gap.

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 classifies recent failed pipelines to provide cause and action per pipeline. It uses specific verbs like 'classify' and 'pulls', and the 'flagship pipeline RCA' distinguishes it from sibling tools like list_pipelines.

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 two usage modes: passing 'failed_pipelines' for pure analysis or a project to pull live. It also notes the 'project' is required unless 'failed_pipelines' is given. However, it does not explicitly contrast with sibling tools or state when not to use.

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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