QA Arbiter MCP for AI. Separate test errors from real code defects.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

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Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

QA Arbiter resolves test failures by forcing deterministic root cause analysis in one call. Stop guessing why a test failed.

This server uses the `diagnose_test_failure` tool to force your agent to trace engine execution step-by-step, compare inputs, and assign a precise verdict: TEST_ERROR, ENGINE_DEFECT, or BOTH_WRONG.

What your AI can do

Diagnose test failure

Forces structured diagnostic for failing tests by requiring step-by-step tracing and comparing three values (Received, Expected, Trace) to assign a deterministic verdict.

Diagnose Test Failure

Forces a deterministic root cause analysis by comparing observed test results against a manually traced engine execution path.

Trace Engine Function Steps

Requires the agent to show every intermediate calculation, branch taken, and value produced during a failing test run.

Identify Test Assertion Errors

Determines if the failure is due to an incorrect expected value set by the test author, independent of code behavior.

Pinpoint Code Defects

Flags failures where the engine's actual output contradicts the required trace, proving a genuine bug exists in the underlying logic.

Validate Logical Consistency

Rejects diagnoses that are internally contradictory (e.g., claiming an engine defect when the received value matches the trace).

Ask an AI about this

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AI Agent

QA Arbiter MCP Server: 1 Tool for Fault Diagnosis

Use the diagnose_test_failure tool to force step-by-step tracing of failing tests, comparing received and expected values against a trace to assign a definitive error verdict.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

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Diagnose Test Failure

Forces structured diagnostic for failing tests by requiring step-by-step tracing and comparing three values (Received, Expected, Trace) to...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The QA Arbiter integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "qa-arbiter": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the QA Arbiter tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"qa-arbiter": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

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Start with QA Arbiter, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
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Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by QA Arbiter. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Debugging failures shouldn't require guessing what the real problem is.

Today, when an automated test fails, developers often enter a loop of guesswork. They check the logs for vague errors, they rerun the test in isolation, and they spend hours debating if the code or the test needs fixing. It’s a manual process that relies on tribal knowledge, not verifiable proof.

With QA Arbiter MCP Server, you force an objective diagnosis. The agent calls `diagnose_test_failure` once. It doesn't just report failure; it traces every calculation and compares it to the expected value. You get a deterministic verdict—TEST_ERROR or ENGINE_DEFECT—in seconds.

QA Arbiter MCP Server: Force Deterministic Test Failure Diagnosis

Manual debugging used to involve copying failed test inputs into a spreadsheet, manually running the function in an interpreter, and then trying to reconcile three separate pieces of data. This was slow, error-prone, and often incomplete.

Now, you let your agent run `diagnose_test_failure`. The server manages the entire multi-pivot comparison and logic check internally. You get a clean verdict that proves *why* it broke—whether it’s an assertion mistake or a genuine code bug.

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What your AI can actually do with this

When an AI agent runs tests, failing results create ambiguity. Is the code broken? Or did the test writer write bad expectations?

QA Arbiter eliminates this guesswork. It forces your agent to use the diagnose_test_failure tool before assuming a fix. This isn't just logging; it’s structured fault diagnosis.

How It Works

The process is rigid: for every failing test, the agent must call diagnose_test_failure. This forces five steps:

Trace: The agent traces the engine function with the exact inputs, showing every intermediate calculation and value produced.
Compare Received: It compares the actual vitest Received value against its own trace. If they match, the engine is working as designed.
Compare Expected: It compares the test's static Expected value against its own trace. This checks if the original assertion was flawed.
Commit Pivots: The agent commits to two boolean flags: receivedMatchesTrace and expectedMatchesTrace.
Verdict: The tool calculates a deterministic verdict from those pivots (e.g., Received=Trace AND Expected≠Trace means TEST_ERROR).

The best part? The tool validates the logic. If your agent tries to declare an ENGINE_DEFECT but marked receivedMatchesTrace: true, the tool rejects the diagnosis immediately, forcing re-analysis.

Built · Hosted · Managed by Vinkius QA Arbiter - Diagnose Test Failures with Structured Reasoning

Server ID 019e5796-a86c-7226-bf18-67f16aeb86a7

Vinkius Inspector

Compliance Grade A+

Score 100/100

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Here's how it actually works

The bottom line is you force structured reasoning to separate test definition errors from actual engine bugs before any code changes happen.

The agent receives a test failure and must call diagnose_test_failure. It starts by tracing the function step-by-step with the inputs.

Next, the agent fills in two boolean pivots: whether the vitest 'Received' value matches its trace, and if the test's 'Expected' value matches the trace.

The tool then computes the final verdict (TEST_ERROR, ENGINE_DEFECT, or BOTH_WRONG) based on those pivots. If there’s a contradiction, the tool rejects it.

Who is this actually for?

This is for the QA engineer who gets sick of ambiguity when tests fail. It's for the software developer tired of chasing phantom bugs and wasting time patching symptoms instead of fixing architectural debt. If your pipeline deadlocks because nobody knows if it’s the test or the code, you need this.

QA Engineer

Runs failing tests through diagnose_test_failure to generate undeniable proof of whether the assertion is flawed (TEST_ERROR) or if the application logic itself broke (ENGINE_DEFECT).

Software Developer

Uses the deterministic verdict from this server to know exactly where to focus: refactoring the test suite when it’s a TEST_ERROR, or fixing the core function when it's an ENGINE_DEFECT.

DevOps/Platform Engineer

Integrates this tool into CI/CD pipelines to stop deadlocks and automatically triage failures without human intervention, knowing whether a failure requires a code push or just a test update.

What Changes When You Connect

Stops deadlocks in multi-agent pipelines. Instead of agents guessing or retrying blindly, diagnose_test_failure forces a structured analysis that yields one of three clear verdicts: TEST_ERROR, ENGINE_DEFECT, or BOTH_WRONG.

Eliminates the 'Fix introduces regression' problem. The tool proves whether the test assertion is wrong (TEST_ERROR) or if the engine genuinely broke, stopping developers from fixing tests instead of code.

Forces detailed reasoning. Your agent can’t skip steps—it must trace the function step-by-step and compare values against two pivots. This prevents superficial debugging and guarantees internal consistency.

Handles complex failure modes. It provides specific guidance on issues like Environment Pollution (shared state) or Flaky Test Tolerance, forcing you to quarantine intermittent failures instead of ignoring them.

Provides verifiable evidence. The tool validates the logical consistency of the diagnosis. If your agent lies about the pivots, the server rejects it, ensuring the final verdict is trustworthy.

See it in action

01 01

A test fails because the expected value was wrong.

The QA engineer runs a failing test through diagnose_test_failure. The tool returns: Received=Trace and Expected≠Trace. The verdict is TEST_ERROR. The agent immediately tells the developer, 'It’s not the code; fix the expectation here.' No time wasted on debugging the engine.

02 02

The core function has a bug (e.g., midnight crossover).

A test fails because of an overflow or domain logic issue. Running it through diagnose_test_failure confirms that Received≠Trace and Expected=Trace. The verdict is ENGINE_DEFECT. This proves the code needs fixing, not the test.

03 03

The pipeline gets stuck guessing which component failed.

Instead of having three separate agents argue over a failure, one agent calls diagnose_test_failure. The resulting structured verdict (e.g., BOTH_WRONG) gives the entire team an immediate, single point of focus: fix both components.

04 04

Debugging intermittent 'flaky' test failures.

A test only fails sometimes. Running it through diagnose_test_failure forces the agent to examine the intermediate calculations and compare them to a stable trace, revealing if the failure is due to shared state (Environment Pollution) rather than logic.

The honest tradeoffs

Patching Symptoms

Anti-pattern

The developer sees a test fail and assumes the code must be wrong. They 'fix' the function output to match the broken test, even though the original bug was in the test assertion.

The Fix

Don’t touch the code until you run diagnose_test_failure. If the result is TEST_ERROR, you only update the test expectation, leaving the engine logic alone. This prevents regressions.

Ignoring Intermediate Steps

Anti-pattern

The agent writes a vague diagnosis like 'something went wrong with the date calculation' without showing the actual math or values involved.

The Fix

You must use diagnose_test_failure and trace every step. Showing the intermediate calculations is mandatory. The tool requires proof, not guesses.

Relying on Passing Tests

Anti-pattern

The team assumes '98% code coverage' means everything is fine. However, passing tests don't prove correctness; they only prove existence.

The Fix

Use this server to verify failure modes. Use diagnose_test_failure on edge cases and known bug vectors. This forces verification of behavior, not just lines executed.

Questions you might have

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.

How does QA Arbiter handle complex data when using diagnose_test_failure? +

The tool requires you to provide a full, step-by-step trace of the engine function execution. You must include every intermediate calculation and value produced by the code logic itself. Simply stating that 'the data processes correctly' is insufficient; the diagnosis depends on arithmetic proof.

What input format does QA Arbiter need for diagnose_test_failure? +

You must provide three specific components: the original failing test assertion (Expected value), the live output from vitest (Received value), and the detailed trace of the engine's internal steps. All inputs are required to calculate the two boolean pivots accurately.

If I get rejected by QA Arbiter, what does that mean? +

A rejection means your proposed diagnosis is logically inconsistent. The tool catches contradictions—for instance, if you claim an engine defect but marked 'receivedMatchesTrace: true'. You must re-examine the intermediate calculations until the reasoning holds up.

Is QA Arbiter limited only to software testing? +

While built for test diagnostics, its core function is structured fault diagnosis. It forces a systematic separation of conflicting data points—a pattern applicable to any domain requiring verifiable root cause analysis beyond simple guesswork.

Connect to your AI in seconds.

Diagnose test failure

QA Arbiter MCP Server: 1 Tool for Fault Diagnosis

Make your AI actually useful.

Diagnose Test Failure

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Debugging failures shouldn't require guessing what the real problem is.

QA Arbiter MCP Server: Force Deterministic Test Failure Diagnosis

What your AI can actually do with this

How It Works

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

A test fails because the expected value was wrong.

The core function has a bug (e.g., midnight crossover).

The pipeline gets stuck guessing which component failed.

Debugging intermittent 'flaky' test failures.

The honest tradeoffs

Patching Symptoms

Ignoring Intermediate Steps

Relying on Passing Tests

When It Fits, When It Doesn't

Questions you might have