How to Use the ENEM Score Calculator MCP in OpenAI Agents SDK

Calculate student performance and predict admission odds directly inside your OpenAI Agents SDK production pipeline.

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Connect ENEM Score Calculator MCP to OpenAI Agents SDK

Create your Vinkius account to connect ENEM Score Calculator to OpenAI Agents SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup ENEM Score Calculator with OpenAI Agents SDK

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Validate ENEM performance with OpenAI Agents SDK

The `calculate_area_averages` tool processes your raw exam data to output the mean performance across the four core subjects. This gives your agent a clean baseline for further analysis without manual math. Your production agent relies on these precise figures to maintain data integrity during multi-step reasoning tasks. It ensures that every calculation remains grounded in the actual exam results.

Weight scores for custom admissions logic

Use `calculate_weighted_score` to adjust how your agent prioritizes specific exam areas based on university requirements. You define the importance, and the agent computes the final result instantly. This approach lets your agent handle complex admission scenarios where certain subjects matter more than others. It keeps your scoring logic dynamic and fully traceable within your agent system.

Predict admission outcomes using historical data

The `simulate_admission_possibility` tool compares a student's final numbers against past cutoff thresholds to estimate the likelihood of acceptance. It turns static scores into actionable insights for your end users. Your agent can now provide real-world context by mapping performance to actual university benchmarks. This helps your system make informed decisions based on historical trends rather than guesses.

Setup guide

Set up ENEM Score Calculator 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 ENEM Score Calculator tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate ENEM Score Calculator 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="ENEM Score Calculator Agent",
            instructions="You have access to ENEM Score Calculator tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ENEM Score Calculator. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ENEM Score Calculator MCP in OpenAI Agents SDK

You simply add the MCP to your agent constructor using the MCPServerStreamableHttp class. Once connected, your agent automatically discovers all three tools for immediate use in your workflows.

Yes, it integrates fully with your agent's safety constraints. Any output generated by the tools passes through your established validation layers before being sent to the user.

You can loop through your data using the agent's logic to call the calculation tools repeatedly. This allows for efficient handling of large student datasets within your existing infrastructure.

The agent receives a standard error response which you can handle using your existing try-except blocks. This ensures your production pipeline remains stable even during unexpected input issues.

This MCP only processes numeric exam results and historical cutoff data. It does not store or persist any personally identifiable information, keeping your data footprint minimal during execution.

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