How to Use the Veeqo MCP in CrewAI

Q: Can I use CrewAI to monitor Veeqo shipments autonomously?

Absolutely. You can build a crew where one agent runs listshipments and another checks those results against known customer IDs, flagging discrepancies without human intervention.

Q: If I use CrewAI, what kind of data does it touch when creating orders for Veeqo?

The process involves taking order details (from getorderdetails) and using that structured information to execute the API call via createmanualorder. It's a full lifecycle operation.

Build autonomous Veeqo operations using specialized agent teams with CrewAI.

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Connect Veeqo MCP to CrewAI

Create your Vinkius account to connect Veeqo 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 Veeqo with CrewAI

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Automated Order Fulfillment Team (CrewAI)

Assign one Agent to retrieve initial data using `get_order_details`. A second, 'Action' Agent then takes the structured output and calls `create_manual_order`. The agents collaborate using shared memory for continuity. This setup allows you to model complex operations: research (Agent 1) gathers details, while another agent performs the necessary action based on those findings.

Inventory Audit and Reporting (CrewAI)

Assign a 'Scout' Agent to run `list_inventory_products`. A second 'Analyst' Agent then uses that list to call `get_product_details` for specific items. The Analyst compiles the full report. This specialized, two-step process allows you to build sophisticated audit trails without needing a single monolithic function.

Customer and Shipment Monitoring (CrewAI)

Create a monitoring crew: one agent calls `list_customers` for the current roster. A second, separate agent then uses that list to check corresponding shipments via `list_shipments`. They pass the customer context between them. This multi-agent approach handles disparate data domains—people records and physical movements—in a single, autonomous operation.

Setup guide

Set up Veeqo 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 Veeqo tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Veeqo transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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="Veeqo Analyst",
        goal="Access and analyze Veeqo data via MCP.",
        backstory="Expert analyst with direct Veeqo access.",
        tools=mcp_tools,
    )

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

CrewAI's shared memory mechanism is key here. The agent that calls `get_order_details` passes the resulting data object to the next specialized agent, which acts on it. It keeps the context clear throughout the entire session.

Yes. You can assign agents roles: one agent lists products (`list_inventory_products`), and another validates that list against specific criteria, making it an automated audit process.

You'll interact with fields like name, email, and ID when listing customers via `list_customers`. These structured records are the foundation for any subsequent action.

Absolutely. You can build a crew where one agent runs `list_shipments` and another checks those results against known customer IDs, flagging discrepancies without human intervention.

The process involves taking order details (from `get_order_details`) and using that structured information to execute the API call via `create_manual_order`. It's a full lifecycle operation.

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