How to Use the Epic Games EOS MCP in LlamaIndex

Index your Epic Games EOS data into LlamaIndex for grounded, searchable game knowledge.

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Connect Epic Games EOS MCP to LlamaIndex

Create your Vinkius account to connect Epic Games EOS 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 Epic Games EOS with LlamaIndex

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Index Epic Games EOS account profiles

Run `get_account_info` and ingest the results directly into your vector store. Your AI client can then query this index to find player details later. It turns raw API responses into a searchable knowledge base. You no longer have to worry about missing data from past sessions.

Vectorize Epic Games EOS friends lists

Call `get_friends_list` and add the output to your LlamaIndex knowledge graph. This allows your agent to answer questions about specific player connections. Semantic search makes finding specific friends across large lists much faster. It's built for scale.

Searchable Epic Games EOS catalog data

Store results from `search_store_catalog` in your index to maintain a local record of store items. This prevents repetitive API calls during index queries. This approach builds a robust local cache that your agent can query instantly. It's perfect for building RAG applications that need historical pricing.

Setup guide

Set up Epic Games EOS 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 Epic Games EOS 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 Epic Games EOS tools.",
)
response = await agent.run("List recent Epic Games EOS 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 Epic Games EOS. 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 Epic Games EOS MCP in LlamaIndex

You define the tool spec in your client and ingest the tool output into your vector index. It's a standard flow for RAG applications.

The data persists in your vector store once indexed. You control how long that information stays in your local index.

Yes, you can toggle between querying the index or executing fresh tool calls. It depends on your agent configuration.

The server handles all traffic through a zero-trust sandbox. Your data remains encrypted in transit and at rest in your index.

It does. You can use the async tool list to keep your indexing pipelines non-blocking.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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