How to Use the Hotmart MCP in LlamaIndex

Q: Can LlamaIndex search through Hotmart student profiles?

It handles this perfectly. You run liststudents, push the output through an embedding model, and store it. Your agent can then run semantic searches across your entire member base.

Q: Does the Hotmart MCP integration work with LlamaIndex filters?

Yes. You can restrict your agent to only use getsalessummary if you want to prevent it from pulling granular transaction data.

Index your Hotmart MCP Server data into LlamaIndex to build searchable RAG applications for your digital business.

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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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Turn sales history into a queryable index

The `get_sales_history` tool pulls your transaction logs straight into LlamaIndex vector stores. You run the tool, embed the results, and suddenly your past revenue data is searchable via natural language. Instead of manually filtering spreadsheets, your agent queries the index. It uses `get_sales_summary` to ground its answers in actual API data, eliminating hallucinations when you ask about last quarter's performance.

RAG applications for student success

The `get_student_progress` tool feeds module completion stats into your knowledge base. You combine this with `list_students` to build a semantic search engine for your customer support team. When a student emails support, the agent queries the index to see exactly which videos they watched. It retrieves the context instantly, allowing your team to send highly specific, helpful replies without opening the main dashboard.

Map products with the Hotmart MCP Server

The `list_products` tool extracts your entire catalog structure for LlamaIndex to process. The framework converts your course titles and descriptions into embeddings. You add `list_subscriptions` to that same index. Now your RAG application understands the relationship between one-off purchases and recurring memberships, giving you a complete, searchable map of your digital assets.

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

Install llama-index-tools-mcp and set up the BasicMCPClient. Call mcp_tool_spec.to_tool_list_async() to expose the endpoints to your FunctionAgent.

It handles this perfectly. You run list_students, push the output through an embedding model, and store it. Your agent can then run semantic searches across your entire member base.

Yes. You can restrict your agent to only use get_sales_summary if you want to prevent it from pulling granular transaction data.

Hard data beats guessing. Indexing your API responses means your agent answers questions based on actual numbers, not generic training data.

Vinkius manages the connection through a zero-trust architecture. When you fetch names and emails via list_students, the server uses ephemeral tokens that expire immediately after the LlamaIndex ingestion step finishes.

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