HowLongToBeat MCP Server for LlamaIndex 1 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add HowLongToBeat as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to HowLongToBeat. "
"You have 1 tools available."
),
)
response = await agent.run(
"What tools are available in HowLongToBeat?"
)
print(response)
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.
LlamaIndex agents combine HowLongToBeat tool responses with indexed documents for comprehensive, grounded answers. Connect 1 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the HowLongToBeat MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 1 tools from HowLongToBeat
Why Use LlamaIndex with the HowLongToBeat MCP Server
LlamaIndex provides unique advantages when paired with HowLongToBeat through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine HowLongToBeat tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain HowLongToBeat tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query HowLongToBeat, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what HowLongToBeat tools were called, what data was returned, and how it influenced the final answer
HowLongToBeat + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the HowLongToBeat MCP Server delivers measurable value.
Hybrid search: combine HowLongToBeat real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query HowLongToBeat to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying HowLongToBeat for fresh data
Analytical workflows: chain HowLongToBeat queries with LlamaIndex's data connectors to build multi-source analytical reports
HowLongToBeat MCP Tools for LlamaIndex (1)
These 1 tools become available when you connect HowLongToBeat to LlamaIndex via MCP:
search_game_times
Search for game completion times
Example Prompts for HowLongToBeat in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting HowLongToBeat to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHowLongToBeat + LlamaIndex FAQ
Common questions about integrating HowLongToBeat MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 1 tools in under 2 minutes. No API key management needed.