How to Use the HowLongToBeat MCP in OpenAI Agents SDK

Q: How does HowLongToBeat integrate with OpenAI Agents SDK?

You register the server URL in your python script using MCPServerStreamableHttp. The SDK auto-discovers the searchgametimes tool, allowing your agent to call it directly during conversations.

Q: Can I use HowLongToBeat to filter game recommendations in OpenAI Agents SDK?

Yes. Your agent calls the searchgametimes tool to retrieve completion times, then applies Python conditional logic to filter out games that do not fit the user's schedule.

Q: How does OpenAI Agents SDK handle rate limits when querying HowLongToBeat via the MCP Server?

You should set cacheToolsList=True in your python configuration to prevent redundant tool discovery calls. This keeps your agent's overhead low and protects your endpoint from hitting rate limits.

Get real-time game completion hours directly in your OpenAI Agents SDK workflow without manual scraping.

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Connect HowLongToBeat MCP to OpenAI Agents SDK

Create your Vinkius account to connect HowLongToBeat 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 HowLongToBeat with OpenAI Agents SDK

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Filter games by duration in OpenAI Agents SDK

The `search_game_times` tool exposes actual gameplay hours directly to your OpenAI Agents SDK setup. You pass a game title, and the tool returns verified community completion statistics. This keeps your agent from recommending a 120-hour RPG when a user specifically asks for a quick weekend game. Your routing agents inspect these time metrics before handing off tasks. The system auto-discovers this capability using the standard MCP protocol, keeping your workflows fast and predictable.

Guardrails for game recommendation agents

The `search_game_times` tool acts as a data validator for your automated recommendations. When your agent pulls game data, this tool verifies the actual time commitment before displaying it to the user. Because the OpenAI Agents SDK runs with strict pre-execution checks, you can write validation rules for this MCP Server that block recommendations if the playtimes exceed user-defined limits. You get clean, structured numbers instead of unpredictable web scraping results.

Trace playtime queries in your dashboard

The `search_game_times` tool integrates directly into your agentic trace history. Every time your system queries a title, the input and output parameters log cleanly in your OpenAI dashboard. This means you can audit exactly why your agent suggested one game over another. You see the raw JSON response containing main story and completionist hours, removing the guesswork from debugging failed agent runs.

Setup guide

Set up HowLongToBeat 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 HowLongToBeat tools at runtime.
3

Create your Agent

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

Run the agent

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

You register the server URL in your python script using `MCPServerStreamableHttp`. The SDK auto-discovers the `search_game_times` tool, allowing your agent to call it directly during conversations.

Yes. Your agent calls the `search_game_times` tool to retrieve completion times, then applies Python conditional logic to filter out games that do not fit the user's schedule.

You should set `cacheToolsList=True` in your python configuration to prevent redundant tool discovery calls. This keeps your agent's overhead low and protects your endpoint from hitting rate limits.

No. The MCP server returns structured JSON containing main story, main+extra, and completionist times, which your agent parses natively.

This server only transmits the specific video game titles your agent searches for to the community database. It runs inside a secure V8 isolate sandbox, meaning your API keys and agent logs never leave the local execution context.

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