How to Use the Estimation Prover MCP in OpenAI Agents SDK

Q: Can OpenAI Agents SDK handle a rejection from the Estimation Prover?

Yes, the agent catches the validation error when the validateestimation tool rejects a plan. You can configure a retry loop where the agent reads the error details, decomposes the task further, and submits a revised estimate.

Stop your OpenAI Agents SDK system from agreeing to delusional timelines by enforcing reality-based estimation checks.

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Connect Estimation Prover MCP to OpenAI Agents SDK

Create your Vinkius account to connect Estimation 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 Estimation Prover with OpenAI Agents SDK

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Forcing Scope Decomposition in OpenAI Agents SDK

The `validate_estimation` tool stops your agent from spitting out a blind two-week guess by rejecting any scope block larger than two days. It forces the OpenAI Agents SDK to break the work down into granular, measurable tasks before it can proceed. When the agent attempts to commit to a sprint plan, this MCP Server intercepts the call and runs a strict validation check. You get an immediate rejection if the agent tries to hand off an un-decomposed task to your production pipeline.

Exposing Unknowns and Historical Variance

The `validate_estimation` tool requires the agent to explicitly map out technical unknowns and link them to concrete historical precedents. No more hand-waving or relying on vague past experience claims that fail during production runs. Your agent must cite a specific past project with similar complexity metrics to justify its timeline. If the historical data does not back up the current plan, the server throws a validation error and halts the run.

Enforcing Dynamic Contingency Buffers

The `validate_estimation` tool calculates a strict buffer based on the novelty of the target architecture. It automatically tacks on a minimum 20% buffer for standard builds and spikes up to 60% for unproven integrations. This guardrail prevents your autonomous agents from setting up teams for burnout. By forcing a hard mathematical floor on timelines, the system ensures that optimism bias never makes it to your tracking dashboard.

Setup guide

Set up Estimation 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 Estimation Prover tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Estimation 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 Estimation 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="Estimation Prover Agent",
            instructions="You have access to Estimation 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 Estimation Prover MCP in OpenAI Agents SDK

Install the package and register the MCP Server streamable HTTP endpoint in your server list. Pass the server instance directly to the Agent constructor, and the model will automatically discover the validation tool.

Yes, the agent catches the validation error when the `validate_estimation` tool rejects a plan. You can configure a retry loop where the agent reads the error details, decomposes the task further, and submits a revised estimate.

It integrates directly into multi-agent workflows. You inject this MCP Server into your planning agent before it hands off tasks to execution agents, ensuring no downstream agent receives an unbuffered workload.

The validation tool will fail the request immediately. Your agent must explicitly state every single assumption that must hold true for the timeline to remain valid, or the run halts.

All task descriptions, complexity metrics, and historical precedent numbers remain entirely inside your local V8 isolate sandbox. No data ever leaves the secure Vinkius environment, keeping your internal product roadmaps completely private.

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