Vestiaire Collective MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Vestiaire Collective as an MCP tool provider through the 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 Vestiaire Collective. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in Vestiaire Collective?"
)
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 Vestiaire Collective MCP Server
Connect your Vestiaire Collective seller account to any AI agent and take full control of your luxury resale business through natural conversation.
LlamaIndex agents combine Vestiaire Collective tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through the 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
- Luxury Search — Find authenticated luxury items from brands like Hermès, Chanel, Louis Vuitton, and Gucci with precision
- Advanced Filters — Search by brand, category, condition, price range, color, and material to find exactly what you're looking for
- Price Analysis — Analyze market trends and resale value for specific luxury brands and categories to optimize your pricing
- Inventory Management — List and track your own selling items and dressing room status directly from your agent
- Catalog Discovery — Browse available brands, designers, and categories within the vast Vestiaire Collective catalog
- Authentication & Details — Retrieve full metadata for items including condition, price history vs. new, and material details
The Vestiaire Collective MCP Server exposes 9 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 Vestiaire Collective to LlamaIndex via MCP
Follow these steps to integrate the Vestiaire Collective 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 9 tools from Vestiaire Collective
Why Use LlamaIndex with the Vestiaire Collective MCP Server
LlamaIndex provides unique advantages when paired with Vestiaire Collective through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Vestiaire Collective tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Vestiaire Collective tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Vestiaire Collective, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Vestiaire Collective tools were called, what data was returned, and how it influenced the final answer
Vestiaire Collective + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Vestiaire Collective MCP Server delivers measurable value.
Hybrid search: combine Vestiaire Collective real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Vestiaire Collective 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 Vestiaire Collective for fresh data
Analytical workflows: chain Vestiaire Collective queries with LlamaIndex's data connectors to build multi-source analytical reports
Vestiaire Collective MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect Vestiaire Collective to LlamaIndex via MCP:
analyze_price_trends
Analyser les tendances de prix pour une marque et catégorie (valorisation du luxe)
get_item_details
Consulter un article : marque, état, authentification, prix vs. neuf, taille, matière
list_available_brands
Lister les marques de luxe disponibles (Hermès, Chanel, Louis Vuitton, Dior, etc.)
list_available_designers
Lister les créateurs et collections
list_catalog_categories
Lister les catégories (sacs, chaussures, vêtements, accessoires, bijoux, montres)
list_my_selling_items
Consulter les articles en vente dans votre dressing
search_by_brand
) et catégorie. Rechercher par marque de luxe et catégorie optionnelle
search_luxury_items
Fournissez une requête textuelle. Rechercher des articles de luxe par mots-clés (ex : "Hermès Birkin", "Chanel tweed")
search_with_advanced_filters
Fournissez les filtres sous forme de paramètres. Recherche avancée avec filtres multiples : marque, état, prix, couleur, matière, pays
Example Prompts for Vestiaire Collective in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Vestiaire Collective immediately.
"Search for vintage Hermès Birkin bags in very good condition under 15000 EUR."
"What is the current resale trend for Chanel Flap Bags?"
"List all items I currently have for sale."
Troubleshooting Vestiaire Collective MCP Server with LlamaIndex
Common issues when connecting Vestiaire Collective to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpVestiaire Collective + LlamaIndex FAQ
Common questions about integrating Vestiaire Collective 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 Vestiaire Collective 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 Vestiaire Collective to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.