How to Use the InfoVetted MCP in CrewAI

Q: How do I configure InfoVetted tools for my CrewAI agents?

Pass the server URL to the agent's mcps list. You can use a tool filter to restrict the agent to only the tools it needs for its specific role.

Q: Can an agent in CrewAI trigger a manual review of an InfoVetted check?

Yes, the agent calls getvettingrequeststatus and evaluates the output. If it meets your criteria for a review, the agent notifies you via your preferred communication channel.

Deploy autonomous background screening teams using CrewAI to handle research, verification, and escalation.

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Connect InfoVetted MCP to CrewAI

Create your Vinkius account to connect InfoVetted 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 InfoVetted with CrewAI

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Specialized vetting crews for CrewAI

Assign a researcher agent to `list_vetting_requests` and a moderator agent to `cancel_active_vetting`. Your crew collaborates to manage the entire pipeline without your input. This role-based setup ensures tasks are handled by the right agent. You define the hierarchy and let the crew operate independently.

Autonomous candidate screening with MCP Server

Your agents use `create_screening_contact` to ingest applications as they arrive. The crew verifies the data against your standards in real-time. This creates a hands-off hiring process. You only jump in when the crew flags a result for manual review.

Shared memory for CrewAI vetting agents

Agents share context about candidates using `get_contact_details`. When one agent updates a record, the rest of the crew knows immediately. This keeps your team synchronized. You avoid duplicate work and ensure every agent has the latest info on a candidate.

Setup guide

Set up InfoVetted 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 InfoVetted tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the server URL to the agent's `mcps` list. You can use a tool filter to restrict the agent to only the tools it needs for its specific role.

Yes, the multi-agent architecture scales well. You run multiple instances of your screening crew to process large batches of candidates in parallel.

We utilize ephemeral sessions for every request. Once the agent finishes the task, the connection closes, and your candidate's personal information is purged from the agent's memory.

Yes, the agent calls `get_vetting_request_status` and evaluates the output. If it meets your criteria for a review, the agent notifies you via your preferred communication channel.

You monitor the logs directly from the MCP Server. You see every call made to `list_screening_contacts` or `create_new_vetting_check` in your console in real-time.

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