How to Use the Besitos Corp MCP in OpenAI Agents SDK

Wire Besitos Corp mobile gaming rewards directly into your OpenAI Agents SDK production systems with full tracing.

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Connect Besitos Corp MCP to OpenAI Agents SDK

Create your Vinkius account to connect Besitos Corp 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 Besitos Corp with OpenAI Agents SDK

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Track games via OpenAI Agents SDK

The `list_games` and `get_game` tools pull the active mobile game catalog straight from this MCP Server into your agent's context. Your AI client sees exactly which titles are live in the marketplace. You skip writing custom API wrappers and just let the agent map game IDs to active campaigns. Managing incentives happens through `list_offers` and `get_offer`. When your agent needs to validate a specific milestone, it checks the reward tiers directly. The OpenAI tracing dashboard records every tool call, so you know exactly why an agent approved or denied a specific payout.

Audit player reward history

The `list_user_activity` tool extracts a specific gamer's play sessions and milestone progress. Handoffs between specialized agents work perfectly here. A support agent can pull the raw activity logs, then pass the context to a billing agent to verify the exact minutes played. Payout verification relies on `list_user_rewards`. If a user complains about missing virtual currency, your agent checks the historical ledger. Built-in guardrails in the SDK ensure the agent only reads this data instead of attempting unauthorized modifications.

Pull live monetization metrics

The `get_revenue_report` tool exposes real-time financial data for your active reward deployment. You get raw numbers on ad spend and in-app purchases without logging into a dashboard. Your agent digests the summary and pushes daily updates to your internal channels. Engagement tracking uses `get_engagement_report` and `list_campaigns`. The agent evaluates which reward campaigns drive the highest retention. You build a production system that automatically pauses underperforming campaigns based on these exact metrics.

Setup guide

Set up Besitos Corp 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 Besitos Corp tools at runtime.
3

Create your Agent

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

Run the agent

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

asyncio.run(main())

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Besitos Corp. 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 Besitos Corp MCP in OpenAI Agents SDK

Install the package via pip install openai-agents. You initialize the connection using MCPServerStreamableHttp with your Vinkius endpoint URL. Pass the resulting server object in the mcp_servers array when building your agent.

Yes, setting cacheToolsList=True prevents the agent from re-fetching the schema on every invocation. This drops latency significantly for high-volume reward processing.

The tools map perfectly to multi-agent architectures. You can dedicate one agent to fetching get_engagement_report data and another to analyzing the results. They pass the context natively.

The MCP Server returns an error payload that the SDK handles natively. Your agent reads the error and can prompt the user for the correct ID or retry with a search.

This integration touches raw user gaming activity and reward ledgers. Vinkius isolates the connection in an ephemeral V8 sandbox. The zero-trust architecture ensures your endpoint token is only used for the exact list_user_activity call requested, leaving no persistent footprint.

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