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Connect Inversion Thinking Prover to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Stop your AI from agreeing with bad technical designs.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 1 capability

We ship only the actions that matter.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through Inversion Thinking Prover.

  1. 01

    Validate inversion thinking

    This capability runs a structured red-teaming analysis on architectural decisions using Munger-style inversion. It forces the agent to provide a falsifiable hypothesis, identify the exact anti-pattern, define measurable kill criteria, and simulate second-order failures.

Real work

What teams actually run

01

Database Scaling Plans

Validate if a new sharding strategy will actually handle specific request loads before implementation.

02

Cache Strategy Validation

Test if an LRU or LFU eviction policy will cause downstream database pressure during traffic spikes.

03

Microservices Communication

Red-team the connection pooling and timeout settings between services to prevent cascading failures.

04

Disaster Recovery Design

Simulate how a failover mechanism might introduce new latency or consistency issues.

Automate a task

Put Inversion Thinking Prover to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Inversion Thinking Prover works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Inversion Thinking Prover to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

977ms average. Fast in production.

Inversion Thinking Prover is checked daily against the live service.

Daily averagePeak 1245ms
Oct 3Today
Fastest day
690ms
Slowest day
1245ms
14-day trend
Slowing+35%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 1 capability arrives ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Inversion Thinking Prover, so you can see the experience inside your AI.

It does not authenticate your account with Inversion Thinking Prover. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Inversion Thinking Prover Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_On8hh3elZBQecoi5mn0INew0FQYXjDeZ1NKrG7xB/mcp

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 — Inversion Thinking Prover capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "inversion-thinking-prover-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_On8hh3elZBQecoi5mn0INew0FQYXjDeZ1NKrG7xB/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 Inversion Thinking Prover in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Inversion Thinking Prover owners hand off.

This MCP is built for engineers who need to move past surface-level AI suggestions and into rigorous system design.

  • 01

    Software Architects

    Use it to stress-test high-level system designs against inevitable failure modes.

  • 02

    DevOps Engineers

    Hand off infrastructure plans to the agent to find hidden bottlenecks and resource exhaustion risks.

  • 03

    Site Reliability Engineers

    Use the red-teaming capabilities to simulate post-mortem scenarios before a deployment occurs.

FAQ

Questions Inversion Thinking Prover owners ask.

  • 01

    What is inversion thinking?

    It is a mental model popularized by Charlie Munger that involves looking at a problem backward. Instead of asking how to make something work, you ask how to make it fail and then design to prevent those specific failures.

  • 02

    How does this MCP stop AI sycophancy?

    It rejects any response that uses non-deterministic language like 'might' or 'could.' It requires the agent to use hard, measurable metrics and deterministic failure modes.

  • 03

    What happens if my agent's plan is rejected?

    The MCP will return a verdict explaining why the plan failed, such as 'UNFALSIFIABLE_BELIEF' or 'SYCOPHANCY_DETECTED.' You then instruct your agent to refine the plan based on that specific feedback.

  • 04

    Which AI clients can use this MCP?

    Any MCP-compatible client including Claude, Cursor, Windsurf, and VS Code.

  • 05

    Do I need to host this myself?

    No. Vinkius hosts and manages the MCP, so it is ready to use immediately after you connect your client.

  • 06

    Why reject words like 'maybe' or 'could'?

    Because LLMs use modal verbs to distance themselves from critique. True red-teaming requires certainty. The trap forces the AI to say 'This WILL fail because of X'.

  • 07

    What is the difference between an anti-pattern and a red team attack?

    An anti-pattern is a structural bad design choice (like storing raw passwords in a DB). A red team attack is an active exploit or failure mechanism (like exhausting memory via connection pooling) that breaks the system. You must define both.

  • 08

    Why are measurable kill criteria necessary for validation?

    Without measurable metrics, 'failure' is just a subjective opinion. Forcing the agent to define concrete thresholds (such as latency > 350ms, memory usage > 90%, or packet loss > 5%) creates absolute, falsifiable limits. It forces the AI to abandon hand-waving assertions.

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