ApparelMagic 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 ApparelMagic as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 ApparelMagic. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in ApparelMagic?"
)
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 ApparelMagic MCP Server
The ApparelMagic MCP Server provides powerful integration with your ApparelMagic Cloud ERP. Connect your account to your AI agent to streamline apparel manufacturing and distribution. Manage your entire product lifecycle, from initial styles to final shipments, using natural language.
LlamaIndex agents combine ApparelMagic 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
- Order Management — List sales orders and retrieve detailed information including quantities and SKUs.
- Inventory Visibility — Check real-time stock levels for any product or style in your collection.
- Customer Insights — Access customer profiles and history to improve sales and support interactions.
- Shipping & Fulfillment — Track shipments and monitor fulfillment status directly from your chat.
- Product Lifecycle — Browse styles and products to manage your apparel collection efficiently.
The ApparelMagic 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 ApparelMagic to LlamaIndex via MCP
Follow these steps to integrate the ApparelMagic 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 ApparelMagic
Why Use LlamaIndex with the ApparelMagic MCP Server
LlamaIndex provides unique advantages when paired with ApparelMagic through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ApparelMagic tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ApparelMagic tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ApparelMagic, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ApparelMagic tools were called, what data was returned, and how it influenced the final answer
ApparelMagic + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ApparelMagic MCP Server delivers measurable value.
Hybrid search: combine ApparelMagic real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ApparelMagic 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 ApparelMagic for fresh data
Analytical workflows: chain ApparelMagic queries with LlamaIndex's data connectors to build multi-source analytical reports
ApparelMagic MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect ApparelMagic to LlamaIndex via MCP:
get_customer
Get details for a specific customer
get_order
Get details for a specific sales order
get_product
Get details for a specific product
get_shipment
Get details for a specific shipment
list_customers
List customers from ApparelMagic
list_inventory
List inventory levels from ApparelMagic
list_orders
List sales orders from ApparelMagic
list_products
List products/styles from ApparelMagic
list_shipments
List shipments from ApparelMagic
list_styles
List styles from ApparelMagic
Example Prompts for ApparelMagic in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ApparelMagic immediately.
"List the last 5 sales orders in ApparelMagic."
"What is the inventory level for SKU 'TSHIRT-BLUE-L'?"
"Show the details for customer 'CUST-889'."
Troubleshooting ApparelMagic MCP Server with LlamaIndex
Common issues when connecting ApparelMagic to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpApparelMagic + LlamaIndex FAQ
Common questions about integrating ApparelMagic 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 ApparelMagic 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 ApparelMagic to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.