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get_flake_verdict

Determine if a failing test is deterministic or flaky by comparing retry attempt signatures. Returns a verdict to guide which fixes are supported.

Instructions

Say whether a failing test behaves the same way every time, by comparing its retry attempts within one run. If you are debugging a failing test, call get_debug_evidence first — it returns this plus the regression boundary and every artifact link in one call, so calling this separately afterwards repeats work already done. Returns a computed verdict — "deterministic" (every attempt failed with the same signature, so the failure repeats), "flaky" (an attempt passed on retry, so the outcome is not consistent), or "inconclusive" (too few attempts, or the attempts failed differently) — plus the per-attempt signatures behind it. The verdict describes the behaviour, not the cause. It tells you which fixes the evidence cannot support; it does not tell you where the fix goes. Decide that after reading the artifacts, the trace and the code. Needs a test that ran with retries enabled; a single attempt is always inconclusive.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesProject ID (e.g. project_<id>)
testrun_idNoRun scope. Omit to use the most recently started run carrying this case.
testcase_idYesPlaywright pw_test_id of the failing case

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.3

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and meets it: it discloses the computed verdict values, the per-attempt signature output, the requirement for retried runs, and importantly the limitation that the verdict describes behavior, not cause. This prevents an agent from over-interpreting the result.

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 longer than average but every sentence earns its place: purpose, alternative-avoidance, output values, interpretive caveat, and prerequisite. It is front-loaded with purpose and remains structured rather than rambling.

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?

For a tool with no output schema, it fully explains what is returned (a verdict plus per-attempt signatures), the conditions required to produce a meaningful result, and how the result should and should not be used. It also handles the sibling-tool relationship explicitly, leaving little for the agent to infer.

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

Parameters3/5

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

The input schema already documents all three parameters at 100% coverage, so the baseline is 3. The description adds behavioral context around retry runs and single-attempt inconclusiveness, but does not materially extend the meaning of projectId, testrun_id, or testcase_id beyond the 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 opens with a specific, meaningful task — 'Say whether a failing test behaves the same way every time' — and clarifies the exact resource: retry attempts within one run. It also distinguishes itself from the sibling get_debug_evidence, so an agent can tell them apart immediately.

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?

It gives explicit routing guidance: if debugging a failing test, call get_debug_evidence first, and explains that calling this tool separately afterwards repeats work. It also states the prerequisite that the test must have run with retries enabled, with the concrete consequence that a single attempt is always inconclusive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.