How to Use the HowLongToBeat MCP in LlamaIndex

Index raw game completion data directly into your LlamaIndex vector store to ground your gaming RAG applications in real numbers.

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Connect HowLongToBeat MCP to LlamaIndex

Create your Vinkius account to connect HowLongToBeat to LlamaIndex 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 LlamaIndex

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Index live playtime data for semantic search

Stop letting your agent guess how long a game is based on outdated training data. This MCP Server lets your LlamaIndex pipeline pull fresh completion times directly into your document indexes for accurate retrieval. By using `search_game_times`, your RAG system can append actual gameplay hours to your game catalogs before vectorizing them. Users get answers grounded in real, live data instead of hallucinated completion estimates.

Query past sessions with grounded context

Build a search index that remembers what games your users have already asked about and how long those games take to finish. LlamaIndex stores the outputs of your tool runs so you can run semantic queries over historical search data. When a user asks for something similar to their previous queries, the system doesn't need to hit the external API again. It retrieves the cached `search_game_times` results directly from your vector index, saving you time and API calls.

Filter LlamaIndex game catalogs by completion tier

Set up structured metadata filters in LlamaIndex based on different play styles. The tool returns separate metrics for main story, main plus extras, and completionist runs. Your query engine can map these distinct numbers to metadata tags on your indexed documents. When a user searches for a short story-driven game, the engine uses these tags to exclude 100-hour completionist epics.

Setup guide

Set up HowLongToBeat MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all HowLongToBeat MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to HowLongToBeat tools.",
)
response = await agent.run("List recent HowLongToBeat data")

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Common questions about HowLongToBeat MCP in LlamaIndex

Install the LlamaIndex MCP tools package and initialize the basic MCP client with your server URL. Wrap it in a tool spec and convert it to a tool list that your function agent can use.

Yes, you can load the tool's outputs into a document parser and index them. This allows your agent to perform semantic search over the retrieved game completion times later.

Yes, you can invoke the tool list asynchronously to keep your application responsive. This prevents network latency from blocking other data loading steps in your pipeline.

You can use the allowed tools filter when defining your agent's MCP tool spec. This ensures only specific query engines have permission to look up game lengths.

Only the specific game titles requested during a query are sent to the external statistics database. Your internal catalog structure, vector embeddings, and user profiles remain entirely local to your LlamaIndex environment.

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