How to Use the Birdeye MCP in OpenAI Agents SDK

Q: Can I use OpenAI Agents SDK guardrails to block unauthorized Birdeye replies?

Yes. You can intercept calls to replytoreview using the SDK's validation layer before the payload reaches the API. This prevents your agent from posting unapproved comments to public listings.

Q: How does OpenAI Agents SDK handle multi-location Birdeye queries?

The agent uses listlocations to map your business footprint, then routes specific tasks to location-focused sub-agents. This keeps tool calls small and prevents context window bloat during heavy support runs.

Deploy production OpenAI Agents SDK bots that handle Birdeye reviews and trigger surveys with built-in guardrails.

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Connect Birdeye MCP to OpenAI Agents SDK

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

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Safely reply to reviews using OpenAI Agents SDK

Your agent uses `reply_to_review` to safely respond to customer feedback directly from your OpenAI Agents SDK production pipeline. This setup lets you run OpenAI's native guardrail checks before posting, meaning no rogue AI responses go live to angry customers. The agent pulls negative Birdeye feedback with `list_reviews` to queue up a draft response. By integrating this MCP Server directly into your agent runtime, you get clean execution logs on your OpenAI dashboard for every customer response.

Automate customer check-ins via agent handoffs

Your support agent triggers the Birdeye `checkin_customer` tool to initiate the review cycle when an OpenAI Agents SDK ticket closes. The secondary agent handles this specialized task, keeping your primary agent focused on active troubleshooting. You configure this flow easily by passing the Birdeye server to your OpenAI Agents SDK constructor. It queries `list_surveys` to find the right feedback form and runs the check-in instantly, tracking the entire multi-agent transaction.

Trace Birdeye survey responses in your dashboard

When your agent invokes `get_survey_responses`, the OpenAI Agents SDK tracing dashboard records the exact payload for debugging. This gives you instant visibility into how your OpenAI models interpret survey data. This deep visibility ensures your production systems handle Birdeye customer sentiment data correctly. You get a clear audit trail of every `get_contact` query without writing custom logging wrapper code for your OpenAI agents.

Setup guide

Set up Birdeye 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 Birdeye tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Birdeye 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="Birdeye Agent",
            instructions="You have access to Birdeye 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 Birdeye MCP in OpenAI Agents SDK

Install the SDK, initialize MCPServerStreamableHttp with your Vinkius URL, and pass it to your Agent constructor. Set cacheToolsList to true to keep tool discovery fast and avoid extra network roundtrips.

Yes. You can intercept calls to reply_to_review using the SDK's validation layer before the payload reaches the API. This prevents your agent from posting unapproved comments to public listings.

The agent uses list_locations to map your business footprint, then routes specific tasks to location-focused sub-agents. This keeps tool calls small and prevents context window bloat during heavy support runs.

No. Vinkius handles the authentication handshake on its side. Your Python code only needs one secure endpoint token to run all ten customer experience tools via this MCP Server.

Every review payload and customer contact record runs inside an isolated V8 sandbox. No data is stored on Vinkius servers, protecting your customer names and feedback from exposure.

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