How to Use the FanPower (PickUp) MCP in OpenAI Agents SDK

Plug the FanPower (PickUp) MCP Server into OpenAI Agents SDK to automate fan engagement and track real-time picks.

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Connect FanPower (PickUp) MCP to OpenAI Agents SDK

Create your Vinkius account to connect FanPower (PickUp) 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 FanPower (PickUp) with OpenAI Agents SDK

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Automate event-driven fan interactions

Trigger `get_event_details` and `list_events` to keep your agent updated on live sports activity. Your agent pulls the latest data to decide when to engage the audience. Use `list_questions` to serve dynamic content during matches. The integration ensures your agent acts on fresh data while respecting the safety guardrails configured in your agent system.

Track picks and leaderboard performance

Feed `list_picks` and `get_pick_details` into your agent to analyze fan sentiment in real-time. You get a clear view of how individual choices influence the game. Monitor top performers using `get_leaderboard`. Your agent identifies trends and reports back, allowing you to react to shifting fan dynamics without manual overhead.

Manage rewards and user profiles

Access `list_rewards` and `get_reward_details` to verify which incentives are currently active for your users. Your agent checks eligibility against `get_fan_profile` to ensure accurate delivery. This workflow automates the loop between participation and reward. It keeps your fan engagement stats moving without requiring constant human intervention.

Setup guide

Set up FanPower (PickUp) 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 FanPower (PickUp) tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the necessary packages and pass the server instance to your agent constructor. The SDK automatically detects the tools once you provide the endpoint.

Yes. The SDK handles tool execution failures and provides tracing. You can catch errors in the agent loop and retry specific tool calls as needed.

The server manages all data access via your endpoint token. Your agent only sees the specific fan profile data returned by the API during an active session.

You can enable tool list caching in your agent configuration. This reduces overhead when your agent frequently checks event or question lists.

Check the agent logs for tool execution results. Each response from the server is returned as a structured object you can inspect during debugging.

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