Shoplazza / 店匠 MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Shoplazza / 店匠 as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Shoplazza / 店匠. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Shoplazza / 店匠?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Shoplazza / 店匠 MCP Server
Empower your AI agent to orchestrate your global retail business with Shoplazza (店匠), the premier E-commerce platform for international brands. By connecting Shoplazza to your agent, you transform complex store management and order tracking into a natural conversation. Your agent can instantly list your products, retrieve detailed order information, monitor inventory levels, and even browse store collections without you ever needing to navigate the Shoplazza Admin interface. Whether you are managing a single boutique or a large-scale international operation, your agent acts as a real-time retail assistant, keeping your data accurate and your global sales moving.
LlamaIndex agents combine Shoplazza / 店匠 tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Product Orchestration — List all items in your store, get detailed product metadata, and browse collections.
- Order Management — List and retrieve detailed order information to track fulfillment, payments, and delivery.
- Inventory Monitoring — Retrieve real-time inventory levels for your products to ensure stock availability.
- Customer Insights — Search and manage customer profiles and their purchase history.
- Store Configuration — Access general shop information and monitor configured webhooks.
The Shoplazza / 店匠 MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Shoplazza / 店匠 to LlamaIndex via MCP
Follow these steps to integrate the Shoplazza / 店匠 MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Shoplazza / 店匠
Why Use LlamaIndex with the Shoplazza / 店匠 MCP Server
LlamaIndex provides unique advantages when paired with Shoplazza / 店匠 through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Shoplazza / 店匠 tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Shoplazza / 店匠 tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Shoplazza / 店匠, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Shoplazza / 店匠 tools were called, what data was returned, and how it influenced the final answer
Shoplazza / 店匠 + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Shoplazza / 店匠 MCP Server delivers measurable value.
Hybrid search: combine Shoplazza / 店匠 real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Shoplazza / 店匠 to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Shoplazza / 店匠 for fresh data
Analytical workflows: chain Shoplazza / 店匠 queries with LlamaIndex's data connectors to build multi-source analytical reports
Shoplazza / 店匠 MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Shoplazza / 店匠 to LlamaIndex via MCP:
get_customer
Get customer details
get_inventory_levels
Get inventory levels
get_order
Get order details
get_product
Get product details
get_shop_info
Get shop information
list_collections
List product collections
list_customers
List shop customers
list_orders
List shop orders
list_products
List shop products
list_webhooks
List store webhooks
Example Prompts for Shoplazza / 店匠 in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Shoplazza / 店匠 immediately.
"List all products in my Shoplazza store."
"Show me the last 5 orders from my Shoplazza shop."
"Check the inventory level for item ID 'inv-123456'."
Troubleshooting Shoplazza / 店匠 MCP Server with LlamaIndex
Common issues when connecting Shoplazza / 店匠 to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpShoplazza / 店匠 + LlamaIndex FAQ
Common questions about integrating Shoplazza / 店匠 MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Shoplazza / 店匠 with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Shoplazza / 店匠 to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.