How to Use the Vestiaire Collective MCP in AutoGen

Q: Does the MCP Server support advanced filtering?

The searchwithadvancedfilters tool allows you to specify multiple parameters. These include brand, condition, price range, color, material, and country, enabling highly detailed searches.

Run consensus-driven decisions on Vestiaire Collective with AutoGen.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Vestiaire Collective MCP to AutoGen

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

GDPR Free for Subscribers

Setup Vestiaire Collective with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debating Item Value

You can set up a debate between two agents. One agent might use `get_item_details` to gather facts about an item's condition and authenticity, while another agent uses `analyze_price_trends` to argue for its current market value. The final decision isn't just the average; it’s a consensus achieved after both perspectives challenge each other, leading to a much more informed conclusion about the listing.

Negotiated Search Parameters

Need to find a specific item? Use AutoGen to make agents negotiate search criteria. One agent might suggest running `list_available_brands`, while another pushes back, suggesting that filtering by color (a parameter for `search_with_advanced_filters`) is more efficient. The system converges on the optimal set of parameters, executing a single, highly refined tool call.

Multi-Agent Catalog Mapping

Build an autonomous workflow where agents collaborate to map out the entire catalog. One agent calls `list_available_designers`, another uses that list to populate categories via `list_catalog_categories`. The final output isn't just a simple list; it’s a structured, debated consensus on how the marketplace is organized, ensuring no major category or designer is overlooked.

Setup guide

Set up Vestiaire Collective MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Vestiaire Collective tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Vestiaire Collective_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Vestiaire Collective data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Vestiaire Collective_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Vestiaire Collective data")
    print(result.messages[-1].content)

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 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Vestiaire Collective now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Vestiaire Collective MCP in AutoGen

Instead of simple execution, multiple agents discuss search intent. If you need to find a specific item, one agent might suggest `search_luxury_items`, while another argues for the precision of using `search_with_advanced_filters`. The final result reflects their agreed-upon strategy.

You can set up a debate where one agent retrieves your listed items using `list_my_selling_items`, and another agent assesses the market risk of those specific items by querying price trends. The consensus provides a balanced view.

Yes. You pass the MCP Server tools to your AssistantAgent constructor, allowing multiple agents within your application to interact and debate decisions about finding or valuing goods on Vestiaire Collective.

The `search_with_advanced_filters` tool allows you to specify multiple parameters. These include brand, condition, price range, color, material, and country, enabling highly detailed searches.

The server touches publicly listed item metadata: brands, materials, sizes, and prices. It is designed to interact with public catalog data only.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python