How to Use the Customer Discovery Prover MCP in CrewAI

Q: How do I integrate Customer Discovery Prover into my CrewAI agents?

Add the server URL to your agent's mcps list. Once added, the validatecustomerdiscovery tool becomes available for your agents to call during their assigned tasks.

Deploy a research crew. Use Customer Discovery Prover with CrewAI to automate rigorous market validation.

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Connect Customer Discovery Prover MCP to CrewAI

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

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Assign validation tasks to specialized agents

Give your research agent access to `validate_customer_discovery`. It can now act as a gatekeeper that reviews interview notes before passing them to your analyst agents. This ensures your crew only works with high-quality, evidence-backed data. It stops your team from chasing ghost leads that lack real commitment signals.

Enforce rigorous standards across the crew

Use `validate_customer_discovery` as a mandatory step in your hierarchical execution. If the tool rejects the persona, the crew stops and requests more data. This creates a culture of accountability within your agent team. It forces them to produce documentation that actually proves a market need exists.

Systematize the Mom Test for autonomous teams

Your agents can now conduct discovery that adheres to strict methodological rules. `validate_customer_discovery` prevents them from reporting fake progress based on hypothetical intent. It allows you to build an autonomous operation that validates its own output. You get clear reports that focus on actual user behavior and spending patterns.

Setup guide

Set up Customer Discovery Prover MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Customer Discovery Prover tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Customer Discovery Prover Analyst",
    goal="Access and analyze Customer Discovery Prover data via MCP.",
    backstory="Expert analyst with direct Customer Discovery Prover access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Customer Discovery Prover transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Customer Discovery Prover Analyst",
        goal="Access and analyze Customer Discovery Prover data via MCP.",
        backstory="Expert analyst with direct Customer Discovery Prover access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Customer Discovery Prover transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

Add the server URL to your agent's `mcps` list. Once added, the `validate_customer_discovery` tool becomes available for your agents to call during their assigned tasks.

Yes. By feeding interview transcripts into the tool, the agents can identify distinct buyer groups based on actual pain points rather than just demographic labels.

The tool forces the agent to weigh evidence and look for falsification. If the data is contradictory, the tool flags it as unverified, preventing the crew from drawing premature conclusions.

It is. It supports standard transport methods, allowing for quick setup in both simple agent scripts and advanced hierarchical workflows.

Your interview text is processed in a secure, ephemeral sandbox. The data is only used for the duration of the tool execution and is never stored on our servers.

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