How to Use the kvCORE MCP in CrewAI

Q: How do I expose kvCORE tools to a specific CrewAI agent?

Use the MCPServerHTTP class from crewai.mcp. You can apply a toolfilter so your researcher agent only sees the read-only commands.

Q: How do I connect the MCP Server to my Python environment?

Install crewaitools. Then define the endpoint URL in your agent's mcps array to establish the connection.

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Connect kvCORE MCP to CrewAI

Create your Vinkius account to connect kvCORE 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 kvCORE with CrewAI

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Perplexity

Autonomous Lead Qualification

The `get_lead_details` tool gives your researcher agent the exact background on a specific prospect. That agent analyzes the history and hands the profile to an action agent. The action agent then uses `update_lead_info` to adjust the status and phone number based on the research. You are building a specialized workforce. One agent only reads data, while a completely different agent holds the permission to write changes. They share memory during the session to coordinate the handoff.

Monitor Property Listings with CrewAI

The `list_property_listings` tool feeds active inventory data straight to your market analysis agent. When a new property appears, the agent triggers `get_listing_details` to pull the full metadata. It evaluates the price and location against your strict criteria. This entire process runs in the background. You set the execution to hierarchical mode, allowing a manager agent to oversee the evaluations. The manager decides if the property warrants a notification to the human broker.

Audit Brokerage Tasks

The `list_agent_tasks` tool lets your compliance agent review what the human team is ignoring. It pulls the pending to-dos and cross-references them with `get_agent_profile` to see who is falling behind. The system logs these discrepancies for the weekly review. Setting this up takes minutes. You pass the MCP Server URL directly into the agent configuration array. The framework handles the tool execution and inter-agent communication automatically.

Setup guide

Set up kvCORE 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 kvCORE tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Use the `MCPServerHTTP` class from `crewai.mcp`. You can apply a `tool_filter` so your researcher agent only sees the read-only commands.

Yes. They share context memory during the execution run. The researcher pulls the lead details, and the writer agent uses that exact context to draft the update.

You write a script to trigger the crew on a cron schedule. The agents boot up, check the inventory, and shut down when the task finishes.

Install `crewai[tools]`. Then define the endpoint URL in your agent's `mcps` array to establish the connection.

Property metadata and agent profiles never touch a database on our end. Vinkius operates a zero-trust architecture that routes the payload directly to your Python environment and immediately flushes the cache.

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