How to Use the Customer Discovery Prover MCP in OpenAI Agents SDK

Q: How does Customer Discovery Prover prevent my OpenAI Agents SDK system from hallucinating target markets?

It intercepts the agent's output using the validatecustomerdiscovery tool. The tool rejects generic statements like 'the market needs this' and demands specific interview transcripts. If your agent tries to make up a persona, the server flags the lack of real-world evidence and halts the workflow.

Q: How do I register the validatecustomerdiscovery tool with my OpenAI agents?

You register the MCP Server using the MCPServerStreamableHttp class in your Python code. Pass the server instance inside the mcpservers list when initializing your agent. The SDK handles tool discovery automatically, letting your agent call the validation tool whenever it analyzes customer profiles.

Stop your OpenAI Agents SDK system from acting on fake personas. Force your agents to validate customer discovery with real evidence.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Customer Discovery Prover MCP to OpenAI Agents SDK

Create your Vinkius account to connect Customer Discovery Prover to OpenAI Agents SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Customer Discovery Prover with OpenAI Agents SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Enforce Mom Test Rules in OpenAI Agents SDK

Your OpenAI agents love to agree with bad ideas. When they draft product strategies, they default to lazy personas like 'busy professionals' and hypothetical 'would you pay' questions. This MCP Server stops that. By running `validate_customer_discovery`, your agents must prove their assumptions with raw interview quotes and documented past behaviors. The tool blocks the agent from progressing if it relies on verbal promises. It forces the agent to look for hard commitments like LOIs or scheduled pilots. This keeps your automated market research grounded in reality, not AI hallucinations.

Validate Agent Research with `validate_customer_discovery`

Production systems built on the OpenAI Agents SDK need guardrails before executing downstream tasks. This tool acts as an automated gateway. It analyzes the research data your agent gathers, scoring it against strict validation metrics to ensure you actually have a defined segment. If the agent tries to pass off a generic demographic as a target market, the validation fails. You get a clear, structured breakdown of missing evidence. This lets you build multi-agent workflows where one agent conducts interviews and another enforces strict quality standards before any code gets written.

Audit Segment Data in Multi-Agent Workflows

When handoffs occur between your specialized agents, assumptions often get treated as facts. An agent might tell a copywriter agent that 'SMBs need this tool' without any proof. This MCP Server intercepts those handoffs. It checks the data to make sure 'SMBs' is broken down into specific targets, like Head of Data at Series B startups. By integrating this check directly into your agent definitions, you prevent downstream agents from wasting API tokens. They won't write marketing copy or product requirements for a non-existent market. You get clean, validated customer profiles before your team starts execution.

Setup guide

Set up Customer Discovery 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 Customer Discovery Prover tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Customer Discovery 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 Customer Discovery 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="Customer Discovery Prover Agent",
            instructions="You have access to Customer Discovery Prover tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Customer Discovery 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Customer Discovery Prover now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Customer Discovery Prover MCP in OpenAI Agents SDK

It intercepts the agent's output using the `validate_customer_discovery` tool. The tool rejects generic statements like 'the market needs this' and demands specific interview transcripts. If your agent tries to make up a persona, the server flags the lack of real-world evidence and halts the workflow.

Yes. Your agents can pass raw text from customer interviews directly to the `validate_customer_discovery` tool. The tool parses the text for Mom Test violations, such as leading questions or hypothetical commitments, and returns a strict validation score.

System prompts are soft suggestions that agents regularly ignore under pressure. This MCP Server provides a hard, programmatic barrier. It uses deterministic validation rules to ensure your agents cannot proceed until they provide actual evidence of customer commitment.

You register the MCP Server using the `MCPServerStreamableHttp` class in your Python code. Pass the server instance inside the `mcp_servers` list when initializing your agent. The SDK handles tool discovery automatically, letting your agent call the validation tool whenever it analyzes customer profiles.

Your raw customer transcripts and persona data are processed in a secure, ephemeral V8 isolate sandbox. No data is stored or used for training. Once the validation tool finishes scoring your customer discovery evidence, the container is destroyed, keeping your proprietary market research completely private.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript