How to Use the HowLongToBeat MCP in AutoGen

Let your AutoGen agents debate and agree on the perfect game recommendation based on actual completion times.

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Connect HowLongToBeat MCP to AutoGen

Create your Vinkius account to connect HowLongToBeat to AutoGen 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 AutoGen

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Resolve recommendation debates with hard data

When your agents are arguing over which game to suggest, give them the tools to settle the debate. One agent can advocate for a game's story depth, while another uses this MCP Server to check if the length fits the user's schedule. By calling `search_game_times`, the critic agent can veto a recommendation if the completion time exceeds the user's weekly limit. This consensus-driven approach ensures your team of agents delivers highly realistic suggestions.

Standardize schema translation automatically

Forget about manually mapping data formats between different agent protocols. The AutoGen adapter handles the translation of the server's tool schemas behind the scenes so your agents can use them immediately. The `search_game_times` tool registers with your assistant agents without complex boilerplate. Your agents can immediately understand how to format their queries and parse the returned playtime metrics.

Coordinate multi-agent MCP Server workflows

Set up a pipeline where a researcher agent gathers game names and a coordinator agent looks up their lengths. This division of labor keeps your agents focused on their specific tasks without overloading a single agent. The coordinator agent uses the MCP tool to populate a shared database of playtimes. Other agents can then read this structured data to write reviews, calculate price-to-hour value, or build custom schedules.

Setup guide

Set up HowLongToBeat MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes HowLongToBeat tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="HowLongToBeat_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent HowLongToBeat data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="HowLongToBeat_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent HowLongToBeat data")
    print(result.messages[-1].content)

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 HowLongToBeat. 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 HowLongToBeat MCP in AutoGen

Use the extension package to fetch the MCP Server tools via the HTTP or stdio transport. You then pass the resulting tool list directly into your assistant agent's constructor.

Yes, any agent in your group chat configuration can access the tool if it is included in their allowed list. They can share the retrieved playtime data to make collaborative decisions.

The adapter returns a clean error message back to the executing agent. The agent can then discuss the failure with the rest of the group and decide whether to retry or proceed with placeholder data.

Yes, you can run the server locally using the stdio transport. AutoGen connects to the local process directly, removing any need for external hosting during development.

This MCP Server protects your lookups by processing them inside an isolated runtime sandbox. The only outgoing payload is the text of the game title string, ensuring your internal agent conversation transcripts never leak to the public web.

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