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QA Arbiter MCP, Ready to Go

Stop your AI agents from guessing on failing tests. Use QA Arbiter in Claude or Cursor to get deterministic test diagnostics and fault localization.

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Stop broken tests from stalling your CI/CD pipeline with deterministic fault diagnosis.

QA Arbiter MCP for AI Agents

Works with every AI agent you already use

…and any MCP-compatible client

Cursor AI Code EditorClaude Desktop AppOpenAI Agents SDKVisual Studio CodeGitHub Copilot AI AgentGoogle Gemini AILovable AI DevelopmentMistral AI AgentsAmazon AWS Bedrock

How fast is the QA Arbiter Connector?

873ms Fast
Fast Acceptable Slow

Average time for the server to become ready for requests over the last 14 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this Connector on Vinkius Cloud, and connect it to your AI agent in seconds.

Min 733ms
Average 873ms
Max 1221ms
Trend (improving) ↓ 10%
Daily latency
1221ms 7/12/2026
830ms 7/13/2026
1033ms 7/14/2026
892ms 7/15/2026
872ms 7/16/2026
830ms 7/17/2026
928ms 7/18/2026
909ms 7/19/2026
916ms 7/20/2026
1067ms 7/21/2026
832ms 7/22/2026
733ms 7/23/2026
742ms 7/24/2026
733ms 7/25/2026
7/12/2026 7/25/2026

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AI Agent

What AI agents can do with QA Arbiter: 1 Tool for Automated Test Diagnostics

Use the diagnose_test_failure tool to get a clear verdict on whether a test failure is a code bug or a bad assertion.

Diagnose test failure

QA Arbiter forces your agent to provide a step-by-step trace of an engine function to determine if a test failure is a code bug or a bad assertion.

A Connector is a URL. Vinkius runs it: hosting, security, governance, observability.

You're looking at one of 5,800+ managed Connectors. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.

01

No Shadow AI

Every agent action is visible, approved, and auditable. Nothing runs outside your governance.

02

Absolute agent control

Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.

03

Cost control per token

Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.

04

Managed & monitored infra

We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.

05

Data protection, DLP by design

Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.

06

Token optimization, real savings

Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.

QA Arbiter for Automated Test Diagnostics

This is for the QA automation engineer tired of flaky CI pipelines and the SDET who needs to ensure that 'fixes' actually address root causes instead of just masking bugs.

QA Automation Engineer

They use this to stop flaky tests from clogging the CI/CD pipeline and to quickly identify if a failure is a real regression.

SDET

They use this to ensure that every bug fix is backed by a trace, preventing the 'fix introduces regression' cycle.

Multi-Agent Orchestrator

They use this to prevent agents from entering infinite retry loops when a test fails in an automated pipeline.

Frequently Asked Questions

What does QA Arbiter do for my test suite? +

It diagnoses why your tests are failing by forcing your agent to perform a step-by-step logic trace. This helps you determine if the bug is in your code or just a mistake in the test assertion.

How does QA Arbiter help with flaky tests? +

It identifies timing dependencies and environmental issues that cause tests to pass sometimes and fail others. It helps you move those tests to a quarantine list so they don't break your CI.

Can QA Arbiter tell if my test is wrong? +

Yes, it compares the actual engine output against the trace and your expected value. If the engine did what it was supposed to do but the test expected something else, it flags it as a test error.

Will QA Arbiter find bugs in my code? +

It identifies engine defects by showing exactly where the code's logic deviates from the expected outcome. It provides the proof you need to give to your developers.

Does QA Arbiter run my tests? +

No, it doesn't run the tests for you. It is a diagnostic tool used after a test fails to provide a clear, deterministic reason for the failure.

How does QA Arbiter prevent regressions? +

It ensures that you don't 'fix' a bug by simply changing the test to match the broken behavior. By proving the engine is actually broken, it forces a real code fix.

Does QA Arbiter run my tests or compute expected values? +

No. QA Arbiter performs zero computation and zero side effects. It forces the AI agent to structure its own reasoning into verifiable steps, then validates that the reasoning is logically consistent. Think of it as a reasoning enforcer — like Sequential Thinking, but specialized for test failure diagnosis.

What are Decision Pivots? +

Decision Pivots are minimal, verifiable checkpoints that all correct reasoning paths must pass through — a concept from the ROMA research framework. In QA Arbiter, the two pivots are boolean fields: receivedMatchesTrace (does the engine's output match the hand-traced computation?) and expectedMatchesTrace (does the test's expected value match?). The verdict is derived deterministically from these two booleans, making it impossible to reach a wrong conclusion without contradicting yourself.

How does it prevent pipeline deadlocks in multi-agent systems? +

In a typical QA→Developer pipeline, when tests fail, the system routes back to the developer. But if the tests themselves are wrong (QA's fault), the developer can't fix them — creating an infinite retry loop. QA Arbiter forces the QA agent to determine fault attribution BEFORE the pipeline routes: if it's TEST_ERROR, the QA agent fixes its own tests; if it's ENGINE_DEFECT, it routes to the developer with traced proof. The aggregate summary tells the orchestrator exactly what to do.

What happens if the agent lies about the boolean pivots? +

The consistency validation catches direct contradictions — e.g., if the agent says both values match the trace but chose TEST_ERROR instead of FALSE_ALARM, the tool rejects it. For subtler misrepresentations, the engineTrace field creates an auditable trail: post-hoc analysis can cross-reference the trace against the actual engine source code. The structured format makes deception mechanically harder than with free-form text.

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