HowLongToBeat MCP Server for LangChain 1 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect HowLongToBeat through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"howlongtobeat": {
"transport": "streamable_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,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using HowLongToBeat, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About HowLongToBeat MCP Server
Equip your AI agent with the ultimate gaming library intelligence via the HowLongToBeat MCP server. This integration provides instant access to the world's most trusted source for game completion times. Your agent can search for any video game and retrieve precise timing data for the 'Main Story', 'Main + Extra', and 'Completionist' runs. Whether you're planning your backlog, deciding on your next purchase, or auditing your play style, your agent acts as a dedicated gaming advisor through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with HowLongToBeat through native MCP adapters. Connect 1 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Game Time Search — Find how long it takes to beat any video game.
- Playstyle Comparison — Compare durations for different completion levels (story vs. 100%).
- Release Intelligence — Retrieve world release dates and exact game titles for thousands of entries.
- Backlog Auditing — Summarize expected playtimes for entire lists of games.
The HowLongToBeat MCP Server exposes 1 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect HowLongToBeat to LangChain via MCP
Follow these steps to integrate the HowLongToBeat MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 1 tools from HowLongToBeat via MCP
Why Use LangChain with the HowLongToBeat MCP Server
LangChain provides unique advantages when paired with HowLongToBeat through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine HowLongToBeat MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across HowLongToBeat queries for multi-turn workflows
HowLongToBeat + LangChain Use Cases
Practical scenarios where LangChain combined with the HowLongToBeat MCP Server delivers measurable value.
RAG with live data: combine HowLongToBeat tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query HowLongToBeat, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain HowLongToBeat tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every HowLongToBeat tool call, measure latency, and optimize your agent's performance
HowLongToBeat MCP Tools for LangChain (1)
These 1 tools become available when you connect HowLongToBeat to LangChain via MCP:
search_game_times
Search for game completion times
Example Prompts for HowLongToBeat in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with HowLongToBeat immediately.
"How long does it take to beat the main story of The Witcher 3?"
"Is 'Hades' a short game for a completionist?"
"Compare the completion times for 'Skyrim' and 'Starfield'."
Troubleshooting HowLongToBeat MCP Server with LangChain
Common issues when connecting HowLongToBeat to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersHowLongToBeat + LangChain FAQ
Common questions about integrating HowLongToBeat MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect HowLongToBeat with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect HowLongToBeat to LangChain
Get your token, paste the configuration, and start using 1 tools in under 2 minutes. No API key management needed.