How to Use the Hotmart MCP in LlamaIndex

Q: Can LlamaIndex agents search my Hotmart transaction history?

Yes, once you index the output of listsaleshistory, your agents can perform semantic searches to find specific transactions or identify patterns in buyer behavior.

Index your Hotmart sales data into LlamaIndex for semantic search and grounded answers.

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Connect Hotmart MCP to LlamaIndex

Create your Vinkius account to connect Hotmart 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 Hotmart with LlamaIndex

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Build a searchable Hotmart knowledge base

Use `list_sales_history` to ingest your transaction logs into a vector store. You can then query your own sales data using natural language. LlamaIndex turns these raw logs into meaningful context. You stop searching through CSVs and start asking questions about your revenue.

Index product and commission data

The `list_products` and `list_commissions` tools provide the raw content for your index. Your RAG agent pulls from this structured data to answer specific questions about your business health. This approach grounds your agent in facts. It references actual API responses rather than guessing at your current product catalog.

Query live subscription status

Connect `list_subscriptions` to your LlamaIndex pipeline to keep your knowledge base current. Your agent retrieves the latest status for any user query. It provides real-time visibility. You no longer rely on static reports that go out of date the moment they are generated.

Setup guide

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

You call the tools to fetch your data and pass the results to your document loaders. Once ingested, the data becomes part of your searchable index.

Yes, once you index the output of `list_sales_history`, your agents can perform semantic searches to find specific transactions or identify patterns in buyer behavior.

You can pipe your webhook logs into your index. This allows you to track recent events alongside your historical sales data for a complete view.

Updates occur whenever you trigger your ingestion pipeline. You control the refresh rate to balance data freshness with your API usage.

Your data is handled within a restricted memory space. We ensure that sensitive payment records retrieved via `list_payments` are never persisted to disk or external storage beyond the scope of your current index session.

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