How to Use the ClassPass MCP in LlamaIndex

Q: Can LlamaIndex search ClassPass class details?

Yes, the agent calls getclassdetail to retrieve specific class descriptions and then indexes them. This lets your LlamaIndex query engine answer detailed questions about workout structures.

Q: Does this ClassPass LlamaIndex MCP integration support semantic search?

Yes, LlamaIndex converts the output of tools like getperformance into vector embeddings, allowing you to search booking trends semantically.

Q: How do I filter specific ClassPass tools in the LlamaIndex MCP setup?

Define an allowedtools list when initializing your McpToolSpec to limit the agent's access. You can restrict the engine to only pull listlocations if you want to prevent performance data indexing.

Q: How does LlamaIndex protect my ClassPass reservation logs?

All reservation logs retrieved via listreservations are processed locally within your secure LlamaIndex pipeline. Vinkius isolates the MCP connection, preventing any external leakage of your customer booking history.

Index your live ClassPass schedule and performance data with this MCP Server for LlamaIndex vector search.

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Connect ClassPass MCP to LlamaIndex

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Setup ClassPass with LlamaIndex

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Grounding LlamaIndex RAG queries in ClassPass profiles

This MCP Server provides the `get_venue_info` tool to feed venue metadata directly into your LlamaIndex vector store. Your LlamaIndex QA agent queries this index to answer specific questions about ClassPass studio amenities without hallucinating details. By embedding the output of `list_locations` alongside your internal policy documents, LlamaIndex ensures customers get accurate location data. The LlamaIndex framework updates these embeddings automatically whenever the ClassPass class schedule changes.

Semantic search over ClassPass reservations

The `list_reservations` tool pulls active booking data so LlamaIndex can index your current attendee list. Users search past ClassPass reservation trends using LlamaIndex natural language queries instead of writing complex database calls. Combining these ClassPass reservation files with `get_class_detail` outputs builds a searchable LlamaIndex knowledge base of customer preferences. Your LlamaIndex pipeline retrieves this context to personalize follow-up emails based on actual ClassPass attendance.

Indexing ClassPass inventory for real-time queries

The `list_inventory` tool exposes your SmartSpot availability directly to LlamaIndex query engines. This allows your LlamaIndex agent to check ClassPass spot counts against historical attendance patterns stored in your vector database. By running ClassPass `get_performance` queries and indexing the results, the LlamaIndex system tracks which classes fill up fastest. LlamaIndex uses this indexed data to suggest optimal times for opening new ClassPass spots.

Setup guide

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

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Common questions about ClassPass MCP in LlamaIndex

Use pip install llama-index-tools-mcp to load the MCP Server tools into your LlamaIndex environment. Call list_schedule to fetch the raw class data, then pass those documents to your vector index.

Yes, the agent calls get_class_detail to retrieve specific class descriptions and then indexes them. This lets your LlamaIndex query engine answer detailed questions about workout structures.

Yes, LlamaIndex converts the output of tools like get_performance into vector embeddings, allowing you to search booking trends semantically.

Define an allowed_tools list when initializing your McpToolSpec to limit the agent's access. You can restrict the engine to only pull list_locations if you want to prevent performance data indexing.

All reservation logs retrieved via list_reservations are processed locally within your secure LlamaIndex pipeline. Vinkius isolates the MCP connection, preventing any external leakage of your customer booking history.

Use it with your favorite AI tools

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