How to Use the Junip MCP in OpenAI Agents SDK

Q: How do I connect the Junip MCP Server to OpenAI Agents SDK?

Install openai-agents via pip. Create an MCPServerStreamableHttp instance with your endpoint URL. Pass it to your Agent constructor using the mcpservers argument.

Q: Can OpenAI Agents SDK read Junip product metrics?

Yes. The agent calls getproduct to read the performance summary of a specific item. It pulls the aggregate data directly from your store.

Q: How does Junip work with OpenAI Agents SDK guardrails?

You define validation rules in Python. When the agent uses listreviews to read customer feedback, the SDK intercepts the output to verify it meets your safety thresholds before proceeding.

Build production-ready review management agents with the Junip MCP Server and OpenAI Agents SDK.

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Connect Junip MCP to OpenAI Agents SDK

Create your Vinkius account to connect Junip 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 Junip with OpenAI Agents SDK

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Audit Junip store reviews with OpenAI Agents SDK

`list_reviews` returns ratings, review content, and reviewer names straight to your agent. Your OpenAI agent can pull the last 1,000 product reviews and run them through built-in guardrails before drafting any public responses. You get full visibility into what the agent actually sees via the OpenAI tracing dashboard. Analyzing specific feedback requires exact metadata. Calling `get_review` gives your agent the custom question responses and attached photo links. If a one-star review triggers a safety constraint, the SDK blocks the action and hands it off to a human escalation queue.

Track request campaigns

`list_campaigns` lets your agent read active efforts to collect new customer reviews. You pass the review tools into your Agent constructor and the SDK auto-discovers the endpoints. The agent checks which campaigns are currently running before deciding to send follow-up emails. Store performance depends on knowing what items actually have feedback. Your system runs `list_products` to pull product names, IDs, and aggregate review metrics. Setting `cacheToolsList=True` keeps the performance tight when checking hundreds of SKUs during a batch job.

Moderate Q&A automatically

`list_questions` returns question text, status, and associated products for merchant response. You can configure a specialized OpenAI agent just for handling customer inquiries. The agent spots unanswered questions and queues them up. Before writing an official reply, the agent calls `get_question` to read the exact context. It then runs `list_answers` to audit previous response quality. You define the safety constraints, ensuring the agent never hallucinates store policies when interacting with the Junip MCP Server.

Setup guide

Set up Junip 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 Junip tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Junip 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="Junip Agent",
            instructions="You have access to Junip tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Junip MCP in OpenAI Agents SDK

Install `openai-agents` via pip. Create an `MCPServerStreamableHttp` instance with your endpoint URL. Pass it to your Agent constructor using the `mcp_servers` argument.

Yes. The agent calls `get_product` to read the performance summary of a specific item. It pulls the aggregate data directly from your store.

You define validation rules in Python. When the agent uses `list_reviews` to read customer feedback, the SDK intercepts the output to verify it meets your safety thresholds before proceeding.

It does. Once you pass the MCP Server instance, the SDK maps all 10 tools automatically. Use the cache flag to speed up initialization.

This setup processes customer names, review text, and uploaded media links. Your server runs locally or on your own infrastructure. OpenAI only sees the specific question or review data you explicitly allow the agent to fetch via the MCP connection.

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