How to Use the Shoplazza MCP in CrewAI

Q: How can CrewAI manage multiple Shoplazza customer accounts?

You define a 'Customer Agent' whose sole job is to call getshoplazzacustomer and listshoplazzacustomers. The crew keeps track of which customers have been queried.

Q: Can I use the MCP Server to list all Shoplazza store orders with CrewAI?

Yes. You can dedicate an agent solely to calling listshoplazzaorders. It aggregates results from this tool and passes them to another agent for analysis.

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Connect Shoplazza MCP to CrewAI

Create your Vinkius account to connect Shoplazza to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Shoplazza with CrewAI

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Autonomous Inventory Management Team

Assign one agent to manage product data. This agent uses `list_shoplazza_products` to get the current inventory list and another to execute `get_shoplazza_product` when details are needed for a specific promotion. A second agent can then use `create_shoplazza_product` to simulate new stock arriving, allowing the team to autonomously update product listings.

Customer Service Escalation Protocols with MCP Server

Set up a 'Support' agent and an 'Admin' agent. The Support agent uses `get_shoplazza_order` when a customer reports an issue. If the order details are incomplete, it passes the failure state to the Admin agent for manual review using shared memory. The outcome is a closed-loop system: problem reported -> data gathered -> specialist takes action.

Market Research and Shop Metadata Analysis

Create a 'Researcher' agent that calls `get_shoplazza_shop` to pull current market metadata. A second 'Strategy' agent then uses this data, along with `list_shoplazza_collections`, to generate suggestions for new product grouping strategies. The team collaborates: the researcher gathers facts; the strategist makes recommendations based on those facts.

Setup guide

Set up Shoplazza 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 Shoplazza tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You define a 'Customer Agent' whose sole job is to call `get_shoplazza_customer` and `list_shoplazza_customers`. The crew keeps track of which customers have been queried.

Yes. You can assign different tools to specialized agents. For example, one agent handles orders while another only handles product creation via `create_shoplazza_product`.

The server exposes customer PII (names, emails), comprehensive order records, detailed product specifications, and global shop settings.

Yes. You can dedicate an agent solely to calling `list_shoplazza_orders`. It aggregates results from this tool and passes them to another agent for analysis.

The crew framework allows you to scope permissions. If a team only needs product data, you can restrict access to `list_shoplazza_products`, keeping customer PII out of the workflow.

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