How to Use the Customer Discovery Prover MCP in AutoGen

Q: How do I register the Customer Discovery Prover with AutoGen?

Use the mcpservertools adapter to fetch the tools from the server. You then register the validatecustomerdiscovery tool directly with your AutoGen AssistantAgent.

Q: How does the Customer Discovery Prover stop AutoGen agents from fabricating personas?

The validatecustomerdiscovery tool actively blocks generic demographic descriptions. If an agent tries to pass off an imaginary persona, the tool returns a validation failure demanding real interview quotes.

Let your AutoGen agents debate customer feedback and demand proof before building with this MCP Server.

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Connect Customer Discovery Prover MCP to AutoGen

Create your Vinkius account to connect Customer Discovery Prover to AutoGen — 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 AutoGen

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Run debate-driven market validation in AutoGen

Equip your skeptical AutoGen agents with the `validate_customer_discovery` tool to ensure no unverified feature ideas slip through. Set up an AutoGen conversation where a product manager agent debates a skeptical researcher agent.

Enforce the Mom Test via agent negotiation

Run the `validate_customer_discovery` tool to check if the user actually committed time or money, while your other AutoGen agents analyze sentiment. This MCP Server acts as the ultimate truth machine in your agentic workflow.

Separate buyer segments through agent consensus

Call the `validate_customer_discovery` tool to split feedback into distinct, realistic buyer segments within your AutoGen conversations. The tool rejects lazy groupings like 'SMBs' and forces the agents to identify specific tech stacks and budgets.

Setup guide

Set up Customer Discovery Prover MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Customer Discovery Prover tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Customer Discovery Prover_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Customer Discovery Prover data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Customer Discovery Prover_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Customer Discovery Prover data")
    print(result.messages[-1].content)

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

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Common questions about Customer Discovery Prover MCP in AutoGen

Use the mcp_server_tools adapter to fetch the tools from the server. You then register the `validate_customer_discovery` tool directly with your AutoGen AssistantAgent.

Yes, you can share the tool across your entire agent group. This allows your researcher agent and your product manager agent to query the same validation logic during their debate.

The `validate_customer_discovery` tool actively blocks generic demographic descriptions. If an agent tries to pass off an imaginary persona, the tool returns a validation failure demanding real interview quotes.

It supports both standard input/output and Streamable HTTP transports. AutoGen handles the schema translation behind the scenes using its built-in adapters.

All interview text and customer notes are processed locally within the Vinkius zero-trust sandbox. Your proprietary discovery data never leaks to external databases or third-party training pipelines.

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