How to Use the Mercado Livre Catalog MCP in OpenAI Agents SDK

Deploy production agents with the Mercado Livre Catalog MCP Server using the OpenAI Agents SDK for reliable listing management.

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Connect Mercado Livre Catalog MCP to OpenAI Agents SDK

Create your Vinkius account to connect Mercado Livre Catalog to OpenAI Agents SDK 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 Mercado Livre Catalog with OpenAI Agents SDK

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Automated listing synchronization

Your agent manages inventory updates without manual intervention. By calling `update_stock` and `update_price` inside your agent loop, you keep your marketplace data in sync with internal databases. Use the SDK guardrails to validate these calls before they hit the API. This ensures that only authorized price changes pass through your pipeline.

Schema-aware item creation

Fetching domain attributes before listing items is mandatory for compliance. Your agent uses `get_domain_attributes` to pull required fields, ensuring every call to `create_item` meets platform standards. This workflow prevents rejected listings. You get immediate feedback through the agent trace if a required attribute is missing.

Real-time catalog oversight

Query your existing listings through `search_items` to verify current status. When you need to adjust details, `update_item` handles title and description changes in one pass. This MCP Server provides the granular control your agent needs to audit the catalog. You can even use `delete_item` to pull listings that no longer meet your quality bar.

Setup guide

Set up Mercado Livre Catalog MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Mercado Livre Catalog tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Mercado Livre Catalog tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Mercado Livre Catalog tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Mercado Livre Catalog Agent",
            instructions="You have access to Mercado Livre Catalog tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 Mercado Livre. 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 Mercado Livre Catalog MCP in OpenAI Agents SDK

You initialize the connection using the MCPServerStreamableHttp class. Pass the endpoint URL to your Agent constructor as part of the mcp_servers list. The SDK automatically maps the tools into your agent's available action space.

Yes. The SDK handles async execution by default, which is critical for managing multiple listing updates. You should use a context manager to ensure the connection lifecycle is handled correctly.

Performance remains high if you enable cacheToolsList=True. This prevents the agent from re-fetching the tool definitions on every turn. Your agent spends less time negotiating the schema and more time executing updates.

Errors bubble up through the standard agent execution trace. If an API call fails, the response includes the specific reason, such as invalid parameters or rate limits. You handle these in your agent logic just like any other Python exception.

Your agent only interacts with the API via the secure token you provide at the endpoint. The server handles authentication, meaning your credentials never touch the agent's logic. All traffic is scoped to your specific seller account.

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