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Create Test Manager Test Run

tm.create_testRun

Creates an empty test run shell in a LambdaTest project with a required title and optional objective, tags, or folder placement. Use this to initialize a run before adding test cases via a separate request.

Instructions

Creates just the SHELL of a new LambdaTest Test Manager test run in a project: a title (required), an optional objective, any number of tags (zero or more), and an optional folder_id to place it inside a test-run folder (from tm.get_testRunFoldersByProjectId - this is the test-run folder tree, separate from test case folders) instead of the project root. This always creates the run with ZERO test cases - test cases and their environment assignments are added to the run afterward via a separate PUT request, not this tool. DANGER: an invalid/nonexistent folder_id causes an unhandled server error (HTTP 500) rather than a clean validation error - no run is created in that case (safe to retry), but only pass a folder_id read from tm.get_testRunFoldersByProjectId. Use tm.get_testRunById afterward to confirm the run was created. Do not call this speculatively - creating a test run is a real, persistent action. KANEAI RUNS: set is_auteur_generated: true to create a one-off KaneAI-type run instead of a plain manual run. This only creates the KaneAI-type run shell - it does NOT create or link a KaneAI schedule (schedules are managed by KaneAI itself, not this API). Manual and KaneAI test cases are not interchangeable: once created, only add test cases whose own is_auteur_generated matches this run's, via tm.add_testCasesToTestRun.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
titleYes
folder_idNo
objectiveNo
project_idYes
is_auteur_generatedNo
Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses danger of invalid folder_id causing 500 error, that the run is created with zero test cases, that it's persistent, and that KaneAI runs have matching constraints. It also says 'safe to retry' on error. This is thorough and transparent.

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 long but each sentence adds value. It is front-loaded with the primary purpose, then details optional args, then behavioral notes, then KaneAI specifics. Slightly verbose but well-structured. Could be tightened slightly without losing content.

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 the complexity (6 params, no output schema, no annotations), the description covers the tool's purpose, usage, dangers, and relationships to siblings. It explains the workflow (create shell, then add test cases). Missing explicit mention of return value (no output schema), but otherwise complete.

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

Parameters4/5

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

Schema description coverage is 0%, so description must compensate. It explains title (required), objective (optional), tags (zero or more), folder_id (optional, from specific tool), and is_auteur_generated (boolean for KaneAI). However, project_id is not explicitly described beyond being required, and 'tags' format could be more detailed. Still, it adds significant value over 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 it creates just the shell of a test run, specifying the resource and action. It distinguishes itself from sibling tools like tm.add_testCasesToTestRun by noting that test cases are added separately. The verb 'creates' is specific and the resource 'test run shell' is well-defined.

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 says when to use this tool (to create the shell) and when not to (add test cases later via PUT). It warns against speculative use due to persistence, and advises using tm.get_testRunById afterward to confirm. It also names an alternative for folder_id (tm.get_testRunFoldersByProjectId) and distinguishes manual vs KaneAI runs.

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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