How to Use the Vestiaire Collective MCP in LlamaIndex

Q: What search types are supported by the MCP Server for Vestiaire Collective?

The server supports keyword searches via searchluxuryitems, structured filtering (searchwithadvancedfilters), and browsing specific categories using tools like listcatalogcategories.

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Connect Vestiaire Collective MCP to LlamaIndex

Create your Vinkius account to connect Vestiaire Collective to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Vestiaire Collective with LlamaIndex

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Indexing Item Details

LlamaIndex takes the output of `get_item_details` and makes it searchable context. If you query about a specific combination—say, 'a Chanel bag made of tweed in Paris'—the system searches its index against all available item data. This means your agent doesn't just give an answer; it grounds that answer in the actual API results from the MCP Server. You can query past sessions or configurations and get answers backed by live `get_item_details` information.

Knowledge-Augmented Search

The system combines structured data with documents. When you run a search using `search_by_brand`, the results aren't just listed; they become part of your knowledge base for that query session. This allows you to ask follow-up questions like, 'Based on these items, what are the most common materials?' and LlamaIndex answers using both the tool output and any supporting document context.

Cross-Reference Catalog Data

You can build RAG applications that combine market data with general knowledge. For instance, you use `list_available_brands` to get a list of names, index those names, and then ask the system questions like, 'Which of these brands are known for outerwear?' The resulting vector store allows semantic search across both the live catalog data and your own uploaded documents.

Setup guide

Set up Vestiaire Collective MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Vestiaire Collective MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Vestiaire Collective tools.",
)
response = await agent.run("List recent Vestiaire Collective data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vestiaire Collective. 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 Vestiaire Collective MCP in LlamaIndex

LlamaIndex doesn't just run a query; it indexes the results. If you use `search_with_advanced_filters`, the resulting filtered items become part of the knowledge base, allowing for highly precise follow-up queries.

Yes. By indexing past API results alongside current tool calls (like `analyze_price_trends`), you can query previous sessions to understand how item valuations changed over time, all grounded in the actual recorded data.

The system creates a unified index. You can ask questions that require combining external document knowledge (like brand history) with live API data from the MCP Server, making the answer much more authoritative.

The server supports keyword searches via `search_luxury_items`, structured filtering (`search_with_advanced_filters`), and browsing specific categories using tools like `list_catalog_categories`.

The server touches publicly listed item metadata, such as brand names, materials, condition descriptions, and prices. It never accesses user account passwords or payment information.

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