How to Use the Arbox MCP in LlamaIndex

Q: How do I configure the Arbox MCP Server with LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Vinkius endpoint. Convert those tools using McpToolSpec and pass them directly to your FunctionAgent.

Q: Can LlamaIndex index Arbox class schedules for vector search?

Yes, the agent calls listschedule to pull the latest class calendar and indexes the text. You can then run semantic queries to find classes that match specific member interests.

Q: How does LlamaIndex handle Arbox member engagement scores?

The engine pulls engagement scores via searchmembers and stores them as metadata in your index. This allows your agent to filter search results based on how active a member is.

Q: Can I filter which MCP tools are available in LlamaIndex?

Yes, you can use the allowedtools filter when setting up your tool spec. This lets you restrict the agent to reading schedules while blocking access to payment data.

Q: Where does my Arbox payment data go during RAG queries?

Your billing records retrieved by listpayments are processed in memory within the Vinkius sandbox. LlamaIndex only indexes the specific fields you allow, keeping your gym's financial transactions private and secure.

Stop guessing gym capacity. Index your Arbox schedule and member data directly into LlamaIndex to query live studio operations.

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Connect Arbox MCP to LlamaIndex

Create your Vinkius account to connect Arbox 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 Arbox with LlamaIndex

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Index Arbox schedule data into LlamaIndex

The `list_schedule` tool extracts your live class calendar so LlamaIndex can build a searchable local index. Your agent queries this vector store to find open classes. It matches members to schedules based on past booking habits. Feeding this schedule data into a query engine answers complex booking questions without hitting the live API. You save on API overhead. Search responses stay fast.

Query member profiles using semantic search

The `search_members` tool pulls active profiles and engagement scores to feed your local index. LlamaIndex vectorizes these details immediately. Your agent runs semantic queries to find members who might cancel based on low engagement. You run this against the `get_member` tool to pull deeper profile metrics when the agent flags an account. The agent combines live CRM data with your historical files. This gives you a complete picture of member behavior.

Ground your fitness RAG applications using this MCP Server

This Arbox MCP Server provides live context for your RAG pipelines by exposing real-time data like `list_payments` and `list_products`. Static files are no longer necessary. Your LlamaIndex agent pulls actual retail inventory and billing statuses to answer questions. You initialize the `BasicMCPClient` and convert the tools using `McpToolSpec` to feed your RAG pipeline. The agent checks live inventory before recommending a protein powder. It also verifies membership payment status on the fly.

Setup guide

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

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Common questions about Arbox MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient` with your Vinkius endpoint. Convert those tools using `McpToolSpec` and pass them directly to your `FunctionAgent`.

Yes, the agent calls `list_schedule` to pull the latest class calendar and indexes the text. You can then run semantic queries to find classes that match specific member interests.

The engine pulls engagement scores via `search_members` and stores them as metadata in your index. This allows your agent to filter search results based on how active a member is.

Yes, you can use the `allowed_tools` filter when setting up your tool spec. This lets you restrict the agent to reading schedules while blocking access to payment data.

Your billing records retrieved by `list_payments` are processed in memory within the Vinkius sandbox. LlamaIndex only indexes the specific fields you allow, keeping your gym's financial transactions private and secure.

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