Connect QA Arbiter to ChatGPT, Claude and Gemini

• 01 / 02How to distinguish test errors
• 02 / 02How to validate traces

Fastest day

627ms

Slowest day

1142ms

14-day trend

Stable-3%

QA Arbiter Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_X69TXPHsdjAfNffcyFTnx1dWNUhv3RVHL0mSZM7p/mcpCopy

Claude Desktop

Follow the steps below to connect in seconds.

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — QA Arbiter capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "qa-arbiter-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_X69TXPHsdjAfNffcyFTnx1dWNUhv3RVHL0mSZM7p/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up QA Arbiter in Claude and other AIs

See all the AI clients this connector works with ↑

• 01

  QA Automation Engineers

  They hand off failing test logs to the agent to get a verified root cause.

• 02

  AI Agent Developers

  They use it to prevent their agents from entering infinite retry loops or deadlocking pipelines.

• 03

  Software Engineers

  They use the deterministic traces to verify if a bug fix is actually necessary.

• 01

  What is a Decision Pivot?

  It is a structured reasoning pattern where the agent must commit to two boolean values regarding whether the received and expected values match the execution trace.

• 02

  How does this prevent agents from guessing?

  The capability requires specific fields like the execution trace. If the agent's verdict contradicts its own trace, the capability rejects the answer, forcing the agent to re-analyze.

• 03

  Can I use this with Claude or Cursor?

  Yes, any MCP-compatible client like Claude, Cursor, or Windsurf can use this MCP once connected through Vinkius.

• 04

  Does the MCP actually run my code?

  No, the capability does not perform computations. It forces the agent to perform the computation and then validates that the agent's reasoning is internally consistent.

• 05

  What are the possible verdicts?

  The capability returns TEST_ERROR, ENGINE_DEFECT, BOTH_WRONG, or FALSE_ALARM.

• 06

  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.

• 07

  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.

• 08

  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.

• 09

  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 capability 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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