How to Use the Delivery Integrity Prover MCP in OpenAI Agents SDK

Q: What happens if verifydelivery rejects the agent's work?

The tool returns False and a list of specific gaps or issues. Your OpenAI agent should be coded to interpret this failure, fix the highlighted problems, and call the tool again until it gets a True response.

Stop your agents from claiming a task is done when it's not. A final, mandatory check for the OpenAI Agents SDK.

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Connect Delivery Integrity Prover MCP to OpenAI Agents SDK

Create your Vinkius account to connect Delivery Integrity Prover 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 Delivery Integrity Prover with OpenAI Agents SDK

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Perplexity

Hold Your Agent Accountable

The `verify_delivery` tool forces your agent to stop and think. Before it can mark a task complete, it has to prove it. This means matching every requirement from the original prompt to a specific file it changed or an action it took. This isn't just a suggestion; it's a gate. If the agent's work doesn't pass the check, the tool returns a hard 'no' with a list of what's missing. It’s a built-in guardrail that forces your agent to finish the job, not just get close.

Clear Traces, Faster Debugging

When something goes wrong, you need to know why. The structured output from `verify_delivery` feeds directly into the OpenAI tracing dashboard. You get a clean, itemized report of what the agent checked, the exact file paths it touched, and the logs it reviewed. No more digging through vague agent conversations. You can see the agent's entire validation process in one place. It makes debugging a hundred times faster because you can immediately spot the gap between the prompt and the agent's execution.

Reliable Handoffs with this MCP Server

In a multi-agent system, one agent's sloppy work poisons the whole chain. Use `verify_delivery` as the final step before handing off a task. The first agent runs the check, validates its work, and generates a clean summary of what's done and what's left. The next agent in the sequence gets a verified starting point, not a mess it has to second-guess. This tool ensures that each step in your agent-to-agent workflow is built on a solid, verified foundation.

Setup guide

Set up Delivery Integrity Prover 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 Delivery Integrity Prover tools at runtime.
3

Create your Agent

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

Run the agent

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

It acts as a mandatory tool call at the end of a task. Your agent uses `verify_delivery` through its MCP connection to prove it met all prompt requirements, and the tool's boolean response gates whether the agent can declare the task finished.

The tool returns `False` and a list of specific gaps or issues. Your OpenAI agent should be coded to interpret this failure, fix the highlighted problems, and call the tool again until it gets a `True` response.

Yes, it's perfect for that. This MCP tool acts as a quality gate before one agent hands a task to another. This ensures the receiving agent gets a clean, validated state to work from.

No, it's a complement. Unit tests check your code's logic. This tool checks your agent's execution logic against a specific, dynamic prompt. They solve different problems.

The server only sees the data needed for verification: task objectives, file paths, and validation logs you provide. Vinkius handles authentication with a single token, and each MCP call runs in an ephemeral, sandboxed environment that is destroyed after the request.

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