How to Use the Counterfactual-Variant Prover MCP in Google ADK

Force your Google ADK agents to solve modified puzzles with first-principles logic instead of memorized patterns.

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Connect Counterfactual-Variant Prover MCP to Google ADK

Create your Vinkius account to connect Counterfactual-Variant Prover to Google ADK 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 Google ADK

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Logic validation for Google ADK

The `validate_counterfactual` tool breaks the cycle of model recitation. It forces your agent to map modified variables against classic puzzle rules before generating a result. You gain control over how your agent handles edge cases. No more relying on stale patterns that fail when the rules shift.

Map variables within Google ADK

Call `validate_counterfactual` to force a rigorous comparison between your input rules and standard puzzle templates. It strips out contaminated logic that usually ruins model performance. Your agents act on the specific data you provide. This ensures the output reflects your unique business requirements rather than generic training data.

Step-by-step proof generation

Use `validate_counterfactual` to generate a verifiable logic chain for every puzzle. It forces the agent to show its work, making it easy to spot where assumptions fail. This is essential when your Google ADK agent needs to provide accurate answers. You get a clear audit trail for every calculation performed.

Setup guide

Set up Counterfactual-Variant Prover MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Counterfactual-Variant Prover tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Counterfactual-Variant Prover_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Counterfactual-Variant Prover tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Counterfactual-Variant Prover. 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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Common questions about Counterfactual-Variant Prover MCP in Google ADK

It does, allowing you to plug logic validation into your existing agent tools. You register it via the McpToolset, making it available for all your Gemini-powered agents.

It prevents the model from defaulting to memorized answers. By forcing a step-by-step proof, it ensures the agent solves the puzzle based on your current inputs.

It supports standard HTTP and Stdio transports used by the framework. You can restrict the tool access using a simple filter if needed.

It is designed to handle custom rules without relying on external training. It gives your enterprise agents the ability to reason correctly under changing parameters.

The server operates as an ephemeral process that handles your input variables only for the duration of the validation. None of your puzzle logic persists on our servers, ensuring your proprietary information stays within your Google Cloud environment.

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