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Get AI Root Cause Analysis for Test Executions

tm.get_testExecutionRCA

Retrieves AI-generated root cause analysis for failed test executions using test IDs, job IDs, task IDs, or stage IDs. Accepts multiple IDs for batch fetching, with optional pagination.

Instructions

Retrieves LambdaTest's AI-generated root cause analysis (RCA) for one or more automation/KaneAI test executions. Accepts any combination of test_ids (the same ID shown as 'Automation Test ID'/test_id by tm.get_testCaseInstancesByTestRunId, tm.get_testExecutionHistoryByTestCaseId, and tm.get_hyperExecuteJobSessions), job_ids (returns RCA for EVERY test execution in that HyperExecute job), task_ids (every execution on that Task), or stage_ids - at least one of the four is required, each as an array (multiple values batch-fetch in a single call). Optional page/limit for pagination over large result sets. Each record includes the RCA itself (category, severity-equivalent root cause/parent failure category, natural-language summary and analysis, a step-by-step error timeline with source logs and stack traces where available, and suggested steps to fix) AND that execution's own job_id/task_id/stage_id/build_id - useful even without needing tm.get_hyperExecuteTestDetails separately. IMPORTANT: RCA only exists for an execution that BOTH actually ran AND failed - a passed execution, an instance that never executed at all, and a wholly invalid ID of any type all return an empty result (not an error), so an empty result here does not necessarily mean an ID was wrong. Only query for executions already known to have failed (e.g. status FAILED on a test case instance that also has a non-empty Automation Test ID / Test URL, confirming it actually reached a session). Read-only; does not modify anything.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
limitNo
job_idsNo
task_idsNo
test_idsNo
stage_idsNo
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: it is read-only, returns empty results for passed/never-executed/invalid IDs (not an error), and details the output structure including RCA fields and associated IDs. It also covers batch fetching behavior and pagination.

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 well-structured with a clear flow: purpose, input parameters, output details, and important caveats. It is somewhat lengthy but each sentence adds value given the tool's complexity. Minor redundancy (e.g., mentioning batch fetching in two places) could be trimmed.

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?

The description is comprehensive, covering inputs, outputs, edge cases, prerequisites, and relationships to other tools. Despite no output schema, it explains the return structure in detail. It handles the complexity of multiple ID types and pagination thoroughly.

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?

The input schema has zero descriptions for its 6 parameters, but the description compensates fully by explaining each parameter (test_ids, job_ids, task_ids, stage_ids, page, limit) in detail, including their meaning, required combinations (at least one of the four ID arrays), and how they relate to other tool outputs.

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 it retrieves AI-generated root cause analysis for test executions, specifying the verb and resource. It distinguishes itself from sibling tools like tm.generate_testExecutionRCA by indicating it retrieves rather than generates, and from other get tools by focusing on RCA.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool (for failed executions), warns against using it for passed or never-executed ones, and provides detailed guidance on how to identify suitable executions (e.g., checking status FAILED and non-empty Automation Test ID). It also explains the different ID types and their contexts.

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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