How to Use the GPTZero MCP in OpenAI Agents SDK

Q: How do I handle false positives using GPTZero in OpenAI Agents SDK?

Use the submitpredictionfeedback tool to send corrections directly back to the platform when a human reviewer overrides a flag. This helps refine future runs and reduces friction in your editorial pipeline.

Q: How does GPTZero protect the text content sent from my OpenAI Agents SDK?

Raw text payloads sent to detectaiintext are processed in ephemeral Vinkius sandboxes and are not stored or used for model training. Your data remains strictly within your execution session, complying with standard enterprise privacy requirements.

Run GPTZero text verification inside your OpenAI Agents SDK pipelines to flag machine-written copy before it hits production.

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Connect GPTZero MCP to OpenAI Agents SDK

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

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Audit content during agent handoffs

The `detect_ai_in_text` tool acts as a silent auditor within your OpenAI Agents SDK pipeline, checking text blocks as they pass between specialized agents. When your writing agent finishes drafting a blog post, the supervisor agent passes that raw text to this MCP Server to verify its origin before triggering the publishing workflow. This setup lets you build strict guardrails using the OpenAI dashboard to trace exactly when and why a piece of content triggered high probability scores. Instead of relying on guesswork, your system gets hard confidence metrics on every run.

Guide agent decisions with interpretation guides

The `get_interpretation_guide` tool provides your OpenAI Agents SDK setup with the exact framework needed to evaluate ambiguous detection scores. Rather than blindly blocking writers based on a raw percentage, your agent reads these rules to decide if a manual review is necessary. You configure the supervisor agent to cross-reference these instructions with the actual detection output. If the text has highly structured technical prose, the agent uses this context to avoid throwing false positives.

Track your API usage in OpenAI Agents SDK

The `get_api_quotas` tool lets your OpenAI Agents SDK instance monitor remaining credits in real time before initiating large batch scanning jobs through the MCP Server. Your agent checks this endpoint to ensure your subscription has enough volume to handle the queue. If the credits run thin, the agent halts the pipeline and notifies your team via the tracing dashboard. This keeps your automated editorial workflow from throwing unexpected API key errors during heavy production runs.

Setup guide

Set up GPTZero 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 GPTZero tools at runtime.
3

Create your Agent

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

Run the agent

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

Your agent auto-discovers all eight endpoints when you register the server during initialization. Simply pass the streamable HTTP transport parameter containing the Vinkius endpoint to your agent constructor.

Yes, but you should chunk texts exceeding 50,000 characters before passing them to the `detect_ai_in_text` tool. The agent can split the content and run parallel checks to avoid HTTP payload limits.

Use the `submit_prediction_feedback` tool to send corrections directly back to the platform when a human reviewer overrides a flag. This helps refine future runs and reduces friction in your editorial pipeline.

You control tool access directly in your Python code by filtering the discovered tools list before passing it to the agent. This prevents the agent from running diagnostic tools like `get_current_user` if they are not needed on this MCP Server.

Raw text payloads sent to `detect_ai_in_text` are processed in ephemeral Vinkius sandboxes and are not stored or used for model training. Your data remains strictly within your execution session, complying with standard enterprise privacy requirements.

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