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How to Use the B5 Arena MCP in LlamaIndex

Index B5 Arena match logs and player stats into your LlamaIndex knowledge base.

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Connect B5 Arena MCP to LlamaIndex

Create your Vinkius account to connect B5 Arena 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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Index B5 Arena data in LlamaIndex

Pass `get_recent_matches` output into your vector store to build a searchable history. Your RAG setup transforms these logs into a queryable knowledge base. This makes past match performance part of your agent's long-term memory. You query the index to get grounded answers about player form.

Retrieve player insights with LlamaIndex

Use `get_user_data` to pull deep stats into your index. Your agent can now answer questions about specific players by searching its own internal data store. It connects live competitive data with your documents. The resulting index provides context that is always up to date.

Search B5 Arena events in LlamaIndex

Index the output of `get_event_list` so your agent stays current on active promotions. Your RAG application retrieves these events as part of its daily briefing. It removes the need for manual checks. You just ask your agent what is happening and it pulls from the index.

Setup guide

Set up B5 Arena MCP in LlamaIndex

Prerequisites

  • Python 3.10+ installed
  • llama-index-tools-mcp package
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install dependencies

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

  2. 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. 3

    Convert to LlamaIndex tools

    Call mcp_tool_spec.to_tool_list_async() to convert all B5 Arena MCP tools into native FunctionTool objects that any LlamaIndex agent can use.

  4. 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 B5 Arena tools.",
)
response = await agent.run("List recent B5 Arena data")

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by B5 Arena. 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 B5 Arena MCP in LlamaIndex

Initialize the tool spec and add it to your agent. Once indexed, your agent treats the match logs like any other document in your store.
The server provides the data, and your agent decides when to re-index. You control the refresh rate of your knowledge base.
No, the tool results are tagged clearly. Your index maintains separation between API-sourced facts and static files.
Yes, use the allowed_tools filter during client setup. This limits exactly which data reaches your index.
We encrypt your connection to the server. Your data stays within your local index instance and is never shared with third parties.

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