How to Use the Shoplazza / 店匠 MCP in CrewAI

Q: How do I list products using Shoplazza / 店匠 with crewai?

You assign an agent to use listproducts. This tool returns a set of product IDs which your collaborating team can then process for further analysis.

Q: Can I research customers on Shoplazza / 店匠 using crewai?

Yes. You let one agent use listcustomers to get the list, and another specialized agent uses getcustomer to pull detailed records for targeted outreach.

Build autonomous teams with crewai. Specialized agents managing Shoplazza / 店匠 collections and customer outreach.

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…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Shoplazza / 店匠 MCP to CrewAI

Create your Vinkius account to connect Shoplazza / 店匠 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 / 店匠 with CrewAI

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Cataloging Product Collections

You can assign one agent to run `list_collections` to map out all product groupings available on Shoplazza / 店匠. A second, specialized agent then takes the returned collection IDs. This second agent uses `list_products` to fetch every individual item within those collections, creating a complete master list for your team's review.

Analyzing Customer Bases

Create a research agent that runs `list_customers` to generate a full roster of shop users. A second agent then uses `get_customer` to deep-dive into specific profiles. The shared memory allows the agents to pass customer IDs and details, ensuring the outreach team only contacts verified accounts.

Auditing Store Connectivity

One agent focuses on running `list_webhooks` to audit all active connections for Shoplazza / 店匠. The second agent then uses `get_shop_info` to cross-reference the shop's current status. The team can automatically flag any webhooks that are listed but whose corresponding shop information suggests the connection might be outdated or inactive.

Setup guide

Set up Shoplazza / 店匠 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 / 店匠 tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You assign an agent to use `list_products`. This tool returns a set of product IDs which your collaborating team can then process for further analysis.

The server handles everything from customer lists to webhook credentials. CrewAI lets different agents work with these varied datasets autonomously, like product details or shop info.

Yes. You let one agent use `list_customers` to get the list, and another specialized agent uses `get_customer` to pull detailed records for targeted outreach.

Absolutely. You give an agent the role of 'Catalog Manager,' and they use `list_collections` to map out all available groupings instantly.

The server exposes customer records, webhook details, and general shop information. These diverse data points allow your autonomous agents to perform multi-faceted audits.

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