How to Use the Clearout MCP in CrewAI

Q: Can CrewAI handle Clearout API connectivity issues?

Yes. You can assign a diagnostic agent to run checkclearoutstatus before kicking off large jobs. If the connection drops, the crew can wait and retry later.

Q: How do I split Clearout tasks among different CrewAI agents?

Give your researcher agent access to findemail and your data engineer agent access to verifybulk and removebulklist. Role-based access keeps your crew focused and prevents accidental deletions.

Deploy autonomous agent teams in CrewAI to clean your marketing lists using Clearout.

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Connect Clearout MCP to CrewAI

Create your Vinkius account to connect Clearout 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 Clearout with CrewAI

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Autonomous List Hygiene Crews

Assign a data-prep agent to manage your massive CSV uploads. This worker triggers `verify_bulk` to start the scrubbing process and hands the tracking ID to a monitor agent. The monitor agent runs `get_bulk_status` on a loop until the job finishes. Once the progress hits 100%, it signals a reporting agent to execute `download_results` and format the clean emails for your next campaign.

Real-Time Lead Enrichment Teams

Build a dedicated research crew that validates inbound leads the second they hit your system. A scout agent takes a company name and runs `find_email` through the MCP Server to locate the right decision-maker. A secondary validation agent then takes that result and runs `verify_email` to ensure the address will not bounce. If the domain looks suspicious, a senior agent steps in to run `verify_catchall` and make a final call on the lead's quality.

Cost Control and Failsafe Operations

Dedicate an oversight agent to watch your API spending. This worker periodically runs `get_credits` and shares the balance with the rest of the crew's shared memory. When credits run dangerously low during a massive batch job, the manager agent can intervene. It will execute `cancel_bulk` to stop the bleeding, preserving the results of already processed emails while halting further spend.

Setup guide

Set up Clearout 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 Clearout tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the Vinkius endpoint directly into your agent's mcps array. For tighter control, use MCPServerHTTP with tool_filter to restrict which agent gets which specific tools.

Yes. You can assign a diagnostic agent to run check_clearout_status before kicking off large jobs. If the connection drops, the crew can wait and retry later.

Give your researcher agent access to find_email and your data engineer agent access to verify_bulk and remove_bulk_list. Role-based access keeps your crew focused and prevents accidental deletions.

Set up sequential execution. Agent A starts the job, Agent B polls the status, and Agent C downloads the results. They pass the list ID down the chain via shared memory.

Vinkius executes every tool call inside a stateless, isolated V8 environment. Your proprietary email lists and domain queries exist only in memory for the duration of the request.

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