How to Use the Counterfactual-Variant Prover MCP in OpenAI Agents SDK

Stop your OpenAI Agents SDK models from hallucinating puzzle answers by forcing first-principles validation.

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Connect Counterfactual-Variant Prover MCP to OpenAI Agents SDK

Create your Vinkius account to connect Counterfactual-Variant Prover to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Counterfactual-Variant Prover with OpenAI Agents SDK

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Decontaminate logic in OpenAI Agents SDK

Your agent stops reciting cached puzzle answers the moment it hits `validate_counterfactual`. It strips away pre-trained biases by forcing a manual mapping of input variables against standard rules. This tool ensures your production pipeline doesn't just guess. It forces the model to calculate every step from scratch, keeping your outputs clean and accurate.

Enforce safety in your agent system

Use the `validate_counterfactual` tool to verify logic before your agent commits to a final answer. It acts as a gatekeeper, checking for contaminated templates that lead to wrong conclusions. You avoid the common trap of model recitation. Your production agents stay grounded in the specific parameters you provide.

Trace every step of the proof

The `validate_counterfactual` tool outputs a step-by-step breakdown of the logic chain. You see exactly how the agent reached its result rather than getting a black-box response. This visibility helps you debug complex puzzles in your OpenAI Agents SDK environment. You get proof that the logic follows your custom rules, not a generic template.

Setup guide

Set up Counterfactual-Variant Prover MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Counterfactual-Variant Prover tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Counterfactual-Variant Prover tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Counterfactual-Variant Prover tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Counterfactual-Variant Prover Agent",
            instructions="You have access to Counterfactual-Variant Prover tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Counterfactual-Variant Prover MCP in OpenAI Agents SDK

It forces the agent to break down logic puzzles instead of guessing from training data. By using `validate_counterfactual`, your agent ignores its internal memory and solves the problem based on the rules you set.

Yes, it integrates directly into your agent constructor. It provides the strict validation your production system needs to avoid hallucinated puzzle solutions.

Models default to reciting classic puzzle answers. This MCP server disrupts that pattern by requiring explicit variable mapping before the agent can finish its task.

Install the server and pass it into your agent definition. It registers as a tool automatically, letting you call validation functions whenever you need.

No, this server handles data in memory and discards it immediately after validation. It never logs your specific variables or puzzle inputs, keeping your proprietary logic private.

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