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Get Test Manager Test Execution History by Test Case ID

tm.get_testExecutionHistoryByTestCaseId

Retrieve the full execution history of a test case by its ID, including pass/fail status, environment details, and execution counts across all test runs.

Instructions

Retrieves the execution history of a LambdaTest Test Manager test case by its exact test case ID: every recorded run's status (passed/failed/skipped/etc.), the test run it belonged to, start/end time, framework, test type (automation/manual), browser/OS/device environment, and automation test ID, plus overall executed/planned execution counts. Use this to inspect how a test case has performed over time. AUTOMATION TEST ID / RCA: for automation/KaneAI executions, automation_test_id is the ID LambdaTest's other services key off - a Test URL is shown (constructed from the same https://automation.lambdatest.com/test?testID={id} pattern this API itself uses elsewhere, since this endpoint does not return a URL field directly) and the same ID can be passed to LambdaTest's AI root-cause-analysis endpoint (https://api.lambdatest.com/insights/api/v3/rca/{automation_test_id}) for a failure's root cause, steps to fix, and error timeline. planned_executions_count is NOT scoped to any single test run - it aggregates across every run/schedule that has ever referenced this test case - so a gap versus executed_executions_count does not indicate anything about a specific run's own instances. Read-only; does not modify anything.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
test_case_idYes
Behavior4/5

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

No annotations are provided, so the description bears full burden. It explicitly states the tool is read-only and does not modify anything. It also clarifies the aggregation behavior of planned_executions_count and explains the automation_test_id field for RCA, which adds useful behavioral context.

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 front-loaded with the core purpose in the first sentence, then expands with useful details. It is somewhat lengthy but well-structured into paragraphs. Each part earns its place, though minor trimming could improve conciseness.

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?

Given no output schema, the description explains return fields thoroughly (status, run, timestamps, environment, counts). It also clarifies edge cases like planned_executions_count aggregation. It lacks mention of pagination or error handling, but covers the main use case well.

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

Parameters2/5

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

The input schema has one parameter (test_case_id) with no description (0% coverage). The description only says 'by its exact test case ID' without specifying format or source, adding minimal value over the schema. For a single parameter, more guidance was expected.

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 specifies the tool retrieves execution history for a given test case ID, listing detailed information like status, run, environment, etc. It distinguishes itself from siblings like get_testExecutionHistoryByJiraId by focusing on test case ID and providing specific return fields.

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 states 'use this to inspect how a test case has performed over time,' giving clear context. It does not explicitly mention when not to use it or compare to alternatives, but provides detailed guidance on the returned data and interpretation of planned_executions_count.

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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