How to Use the Customer Discovery Prover MCP in Google ADK

Q: How does Customer Discovery Prover integrate with Google ADK to analyze massive interview logs?

You configure the McpToolset with the server's HTTP endpoint and pass it to your LlmAgent. This allows your Gemini models to use their long-context window to read hours of transcripts, while the validatecustomerdiscovery tool strictly validates the extracted evidence.

Q: Can Customer Discovery Prover identify leading questions in raw feedback within Google ADK?

Yes, the validatecustomerdiscovery tool is designed specifically to catch biased validation. It flags questions like 'would you pay for this' and forces your Google ADK agent to look for evidence of past behavior and actual workarounds instead.

Run rigorous customer discovery validation inside Google ADK. Force your long-context Gemini agents to verify market demand with real data.

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Connect Customer Discovery Prover MCP to Google ADK

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

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Setup Customer Discovery Prover with Google ADK

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Analyze Million-Token Transcripts in Google ADK

Gemini models excel at processing massive datasets, but they still struggle to spot confirmation bias. When you feed hours of interview recordings into your agent, it might ignore the red flags. Using `validate_customer_discovery` allows your agent to systematically audit large volumes of text for actual customer pain points. The tool extracts concrete evidence of past behavior from your BigQuery data or raw text inputs. It ensures your agent doesn't get distracted by polite, hypothetical feedback. You get a cold, objective assessment of whether your target users are actually trying to solve the problem today.

Enforce the Mom Test on BigQuery Customer Data

If your enterprise agents are querying customer feedback tables, they need a framework to separate noise from signal. This MCP Server applies the Mom Test methodology directly to those datasets. It flags questions that ask for future intent and highlights actual customer commitments instead. By forcing this validation, your agents can identify which product features have genuine backing. They won't recommend building a tool based on verbal promises. You save engineering resources by focusing only on validated, high-priority user pain points.

Segment Validation for Enterprise Agents

Enterprise agents often lump diverse user groups into massive, useless categories. This MCP Server forces your Google ADK agent to split those broad demographics into distinct buyer personas. It checks that each segment has its own documented budget, workflow, and pain points. Your agent can run this check across hundreds of feedback files stored in Google Cloud. If the tool detects that a segment is too broad, it rejects the analysis. This ensures your product-market fit evaluations are always based on narrow, highly qualified customer groups.

Setup guide

Set up Customer Discovery 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 Customer Discovery 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="Customer Discovery Prover_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Customer Discovery Prover tools via MCP.",
    tools=mcp_tools,
)

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.

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Common questions about Customer Discovery Prover MCP in Google ADK

You configure the `McpToolset` with the server's HTTP endpoint and pass it to your `LlmAgent`. This allows your Gemini models to use their long-context window to read hours of transcripts, while the `validate_customer_discovery` tool strictly validates the extracted evidence.

Yes, the `validate_customer_discovery` tool is designed specifically to catch biased validation. It flags questions like 'would you pay for this' and forces your Google ADK agent to look for evidence of past behavior and actual workarounds instead.

Writing custom validation logic for qualitative data is incredibly difficult and prone to edge cases. This MCP Server provides a pre-built, standardized implementation of the Mom Test framework. It gives your Google ADK agents a reliable way to score customer discovery data without writing complex parsing scripts.

The tool looks for hard commitment signals rather than verbal agreements. It requires your agent to find evidence of deposits, letters of intent, or scheduled pilot dates. If these signals are missing from your research data, the tool marks the willingness-to-pay as unverified.

All interview text and segment data sent through the MCP Server are isolated in secure, single-tenant runtimes on Vinkius. Your proprietary customer discovery data is never cached, stored, or exposed to external networks. The data is processed in memory and immediately discarded after validation.

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