How to Use the HowLongToBeat MCP in LangChain

Feed real-time game completion metrics straight into your LangChain reasoning loops to filter recommendations by actual playtimes.

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Connect HowLongToBeat MCP to LangChain

Create your Vinkius account to connect HowLongToBeat to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build time-aware game recommendation chains

Stop recommending massive RPGs to players with only five hours of free time a week. By combining this MCP Server with your LangChain setup, your agent can dynamically look up playtimes and drop any title that doesn't fit the user's budget. Your agent calls `search_game_times` to fetch the average hours required for the main story, extras, or full completion. It then feeds those exact numbers into the next step of your chain to filter out games that are too long or too short.

Trace completion queries with LangSmith

Keep an eye on every single lookup to make sure your agent isn't wasting tokens or hitting API limits. LangSmith traces the entire execution path of your chains, showing you exactly when and why the agent decided to check a game's length. You see the exact input query passed to `search_game_times` and the raw data returned. This visibility helps you debug why a particular game was recommended or excluded during a complex multi-step reasoning run.

Connect multiple MCP Servers in a single run

Combine game length stats with price data or review scores by spinning up a multi-server client. Your LangChain agent can query this MCP Server and then immediately pass the results to a separate database tool to calculate value-per-hour metrics. Setting this up takes just a few lines of code using the multi-server adapter. The agent manages the dependencies between tools, ensuring that `search_game_times` runs first before any financial or review-based math happens.

Setup guide

Set up HowLongToBeat MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes HowLongToBeat tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "howlongtobeat-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent HowLongToBeat transactions"
    })
    print(result["messages"][-1].content)

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Common questions about HowLongToBeat MCP in LangChain

Install the adapters package and use the multi-server client to register the endpoint. You then fetch the tools and pass them directly to your agent's initialization function.

Yes, the agent uses its conversation memory to extract the game name mentioned by the user. It then passes that name to the `search_game_times` tool without requiring manual input.

You can set execution timeouts on your tool calls to prevent slow API responses from hanging your entire chain. If a query takes too long, the agent can catch the error and fall back to default playtime estimates.

You can definitely do that. The agent first retrieves a list of relevant games from your vector store, then loops through them using `search_game_times` to filter the final recommendations by duration.

This server only transmits the game titles you search for to the external database. No personal user identifiers, API keys, or chat histories are ever sent over the network during these lookups.

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