How to Use the Five9 QM MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK agent handle Five9 QM calibration tasks?

Yes. Use listcalibrations to feed current session data to your agent. It compares human scores against its own analysis to maintain consistency.

Q: Does Five9 QM allow my OpenAI Agents SDK agent to pull recording links?

Absolutely. The getrecordinglink tool provides secure access to audio files, which your agent uses to conduct deep-dive reviews.

Automate agent performance reviews in OpenAI Agents SDK using the Five9 QM MCP Server for precision feedback.

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Connect Five9 QM MCP to OpenAI Agents SDK

Create your Vinkius account to connect Five9 QM 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 Five9 QM with OpenAI Agents SDK

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Full evaluation lifecycle via OpenAI Agents SDK

Your agent uses `create_evaluation` to submit scores directly into the system after analyzing interaction transcripts. This closes the loop between raw data and actionable performance records without manual intervention. By leveraging `get_evaluation_form`, the agent understands exactly what criteria matter for each specific department. It ensures that every score submitted aligns with current quality standards.

Instant interaction retrieval for your agent

Your agent calls `get_qm_interaction` to pull metadata for any specific call in the queue. It identifies the context of the conversation before you even ask for a summary. Once the context is set, the agent uses `get_recording_link` to access the actual audio. This allows your agent to process the full interaction instead of relying on incomplete logs.

Manage your agent workforce with MCP Server tools

Use `list_qm_agents` to pull the active roster and target specific individuals for performance coaching. The agent tracks who needs attention and prioritizes them based on recent metrics. Running `list_evaluations` lets your agent pull historical data to spot trends in agent behavior. It identifies coaching opportunities that human supervisors might otherwise miss during their shifts.

Setup guide

Set up Five9 QM 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 Five9 QM tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Five9 QM tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Five9 QM 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="Five9 QM Agent",
            instructions="You have access to Five9 QM 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 Five9 QM MCP in OpenAI Agents SDK

Connect the server to your agent using the SDK's transport layer. Once linked, the agent calls the tools directly to pull performance records into your workflow.

Yes. Use `list_calibrations` to feed current session data to your agent. It compares human scores against its own analysis to maintain consistency.

Absolutely. The `get_recording_link` tool provides secure access to audio files, which your agent uses to conduct deep-dive reviews.

The server returns a standard error code. Your agent must implement retry logic to handle these spikes gracefully.

Yes. The server only touches interaction metadata and evaluation scores. We use ephemeral sessions so no data persists once your agent finishes the task.

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