How to Use the AccelByte MCP in LlamaIndex

Index live AccelByte data into LlamaIndex to build RAG apps that ground agent responses in real-time backend facts.

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Connect AccelByte MCP to LlamaIndex

Create your Vinkius account to connect AccelByte 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 AccelByte with LlamaIndex

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Ground agent knowledge in AccelByte

Pull user details or achievement data using `get_user` and `unlock_achievement`. LlamaIndex vectors these results so your agent answers questions based on actual player history. Stop relying on static training data. Your agent queries the live backend to provide accurate, up-to-the-minute updates on player progress.

Searchable AccelByte backend tools

Convert `list_friends` and `get_leaderboard` outputs into a queryable index. Your agent retrieves specific friend lists or rankings by searching the semantic space of your game data. Combine these results with external documents. The agent finds connections between game rules and specific user records automatically.

Deploy intelligent MCP servers

Use `McpToolSpec` to bridge the gap between your API and the LlamaIndex query engine. This allows your agent to fetch data and reason about it in one pass. Filter available tools to maintain strict control over which actions your agent can perform. You keep the power to modify user accounts while letting the agent read data freely.

Setup guide

Set up AccelByte 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 AccelByte 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 AccelByte tools.",
)
response = await agent.run("List recent AccelByte data")

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Common questions about AccelByte MCP in LlamaIndex

Use the MCP tool spec to wrap the server. This exposes all methods to your agent as standard query tools.

You control the indexing logic. You can store transient tool results in memory or persist them in vector stores for long-term analysis.

Yes. The agent uses the provided tools to retrieve current state data before generating a response. This eliminates the risk of stale information.

We enforce strict credential handling through the MCP standard. Your API tokens are never exposed within the vector index itself.

Your user data remains inside your controlled infrastructure. We treat player IDs as ephemeral tokens, ensuring no PII is stored long-term in your index.

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