How to Use the CUFinder MCP in CrewAI

Q: How do I assign CUFinder tools to specific agents in CrewAI?

You pass the MCP Server URL to the agent's mcps list. You can use a tool filter to ensure only your researcher agent has access to findemployees while others remain restricted.

Run specialized agent crews that research, verify, and enrich CUFinder lead lists autonomously with CrewAI.

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Connect CUFinder MCP to CrewAI

Create your Vinkius account to connect CUFinder to CrewAI 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 CUFinder with CrewAI

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Run specialized crews using this MCP Server

This MCP Server provides the exact tools your specialized CrewAI agents need to divide prospecting labor autonomously. You can set up a Lead Researcher agent to find targets using `find_employees`, while a separate Verification agent runs `verify_email` on the results. This division of labor prevents your prospecting from bottlenecking. The agents pass clean, verified leads back and forth within their shared memory without needing manual handoffs.

Automate account-based intelligence gathering

This MCP Server feeds firmographics and technical stack data to your autonomous CrewAI agents using `get_company_info` and `get_company_tech`. The crew compiles this intelligence into a single report without human intervention. This autonomous research loop runs in the background, preparing your sales team with deep account insights before they make their first call.

Filter and restrict tools for specific agents

This MCP Server allows you to selectively expose prospecting tools like `bulk_enrich` and `find_domain` to specific agents in your crew. This keeps your API costs predictable and prevents agents from going rogue. By managing tool exposure at the agent level, your automated sales operations run exactly how you design them.

Setup guide

Set up CUFinder 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 CUFinder tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent CUFinder 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="CUFinder Analyst",
        goal="Access and analyze CUFinder data via MCP.",
        backstory="Expert analyst with direct CUFinder access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent CUFinder 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 CUFinder. 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 CUFinder MCP in CrewAI

You pass the MCP Server URL to the agent's `mcps` list. You can use a tool filter to ensure only your researcher agent has access to `find_employees` while others remain restricted.

Yes, CrewAI's shared memory allows agents to pass findings down the line. A researcher agent can find a domain using `find_domain`, and a writer agent can immediately use that domain to pull tech stacks.

CrewAI works seamlessly with Streamable HTTP and SSE. Vinkius manages the transport layer, so you only need to supply the connection URL to your crew config.

You do not need to manage individual API keys for your agents. Vinkius handles the authentication on its secure proxy, leaving your Python code clean and credential-free.

All emails, phone numbers, and employee profiles are fetched through an ephemeral, zero-trust connection. The data is delivered directly to your running crew memory and is never logged or stored by Vinkius.

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