How to Use the LeafLink MCP in CrewAI

Q: How do I limit which tools the LeafLink MCP Server exposes to CrewAI?

Use the MCPServerHTTP class from the crewai.mcp package and apply a toolfilter to expose only specific tools like listwholesaleproducts to certain agents.

Deploy a collaborative team of specialized agents to run your wholesale cannabis operations using CrewAI.

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Connect LeafLink MCP to CrewAI

Create your Vinkius account to connect LeafLink 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 LeafLink with CrewAI

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Coordinate CrewAI teams for order fulfillment

Do not rely on a single agent to manage your wholesale cannabis pipeline. With CrewAI, you can set up an inventory agent that monitors stock using `list_wholesale_products` and a logistics agent that handles shipping updates. The CrewAI agents share memory and execute tasks sequentially. Once the inventory agent confirms stock levels, the logistics agent can execute `update_order_status` to move the wholesale order to the next stage.

Specialized product catalog management

Let specialized CrewAI agents manage your LeafLink brand listings autonomously. A researcher agent can identify catalog gaps, while a writer agent prepares details to execute `create_new_product` directly on this MCP Server. To set this up, install using `pip install crewai "crewai[tools]"` and pass the Vinkius URL in the agent's `mcps` array. This keeps your python-based cannabis automation clean and modular.

Autonomous customer and brand auditing

Run scheduled compliance audits across your LeafLink distributor network. Your crew can pull data from `list_wholesale_customers` and cross-reference active inventory via `get_product_details` to flag discrepancies. If an audit fails, the monitoring agent can flag the issue and update the status of pending shipments using `update_order_status` to prevent unauthorized deliveries.

Setup guide

Set up LeafLink 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 LeafLink tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You can pass your Vinkius HTTP URL directly into the agent's `mcps` list during initialization, or use `MCPServerHTTP` for advanced filtering of specific tools.

Yes. CrewAI's shared memory allows different agents to collaborate, so one agent can fetch details with `get_order_details` while another updates the status.

Use the `MCPServerHTTP` class from the `crewai.mcp` package and apply a `tool_filter` to expose only specific tools like `list_wholesale_products` to certain agents.

The integration supports stdio, SSE, and Streamable HTTP transports, making it easy to run your crew locally or in a containerized cloud environment.

Your inventory counts and brand records are queried directly from LeafLink using a zero-trust model. Vinkius isolates the runtime environment, ensuring no wholesale data is cached or exposed.

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