How to Use the RAWG Video Games Database MCP in LlamaIndex

Q: How does LlamaIndex index data from the RAWG Video Games Database?

The framework calls searchvideogames or getvideogamedetails and converts the JSON outputs into document nodes. These nodes are then loaded into your vector index for semantic search.

Index over 500,000 games and platforms directly into your LlamaIndex vector store.

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Connect RAWG Video Games Database MCP to LlamaIndex

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Setup RAWG Video Games Database with LlamaIndex

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Turn game searches into searchable LlamaIndex nodes

The `search_video_games` tool queries the database for titles, release years, and review scores. LlamaIndex takes this raw data and indexes it into your local vector store for semantic querying. This lets you run natural language queries over your past search sessions. Instead of hitting the API repeatedly, your agent searches your indexed knowledge base to find previously retrieved titles.

Build a local knowledge base of game details

The `get_video_game_details` tool extracts deep metadata including developer descriptions, ESRB ratings, and publisher info. Your LlamaIndex pipeline turns this structured payload into document nodes. Your agent queries these nodes to answer specific questions about game mechanics or developer portfolios. Grounding your RAG application in this verified data prevents the LLM from hallucinating game facts.

Map hardware options using this MCP Server

The `list_video_game_platforms` tool lists every console and operating system recognized by the database. LlamaIndex indexes this hardware list to map game compatibility during retrieval. This allows your RAG system to cross-reference user hardware constraints. By matching indexed platform nodes with game detail nodes, your agent filters out incompatible titles before presenting them.

Setup guide

Set up RAWG Video Games Database 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 RAWG Video Games Database 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 RAWG Video Games Database tools.",
)
response = await agent.run("List recent RAWG Video Games Database data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by RAWG. 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 RAWG Video Games Database MCP in LlamaIndex

The framework calls `search_video_games` or `get_video_game_details` and converts the JSON outputs into document nodes. These nodes are then loaded into your vector index for semantic search.

Yes, you can pass an allowed tools list when initializing your MCP tool spec. This lets you restrict your agent to only run `list_video_game_platforms` if you want to save API calls.

Yes, you can use the async tool list method to load the tools. This keeps your query engine responsive while fetching game metadata.

Install the MCP tool library for your framework first. Connect to the Vinkius endpoint, wrap the client in the tool spec, and hand it to your agent.

Yes, this integration only reads public game metadata, platform lists, and studio details. The V8 sandbox ensures no database credentials or query histories are cached on external disks.

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