How to Use the Hotmart MCP in LangChain

Q: Can I use Hotmart with LangChain for automated reconciliation?

Yes, you can. By chaining listsaleshistory and listcommissions, you can build a logic block that identifies discrepancies between expected and actual payouts.

Chain Hotmart data through LangChain agents to automate your entire sales and commission reconciliation pipeline.

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Connect Hotmart MCP to LangChain

Create your Vinkius account to connect Hotmart to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate sales reporting in LangChain

Feed `get_sales_report` results directly into your agent's memory for real-time analysis. Your pipeline treats these numbers as raw input for complex decision-making tasks. Stop manual data entry. Instead, let your agent parse the output and trigger downstream workflows without you lifting a finger.

Track affiliate performance chains

Use `list_affiliates` to pull current partner metrics into your LangChain agent's context. Your agent compares this data against `list_commissions` to identify high-performing segments. Connect these tools to visualize trends. You get instant updates on who is driving revenue without running separate queries.

Manage Hotmart subscriptions programmatically

The `list_subscriptions` tool feeds live member data into your agent's logic. You can now build chains that verify access or flag cancellations automatically. This MCP Server keeps your agent informed. Your logic reacts to every status change immediately, keeping your records accurate.

Setup guide

Set up Hotmart MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Hotmart tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "hotmart-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Hotmart transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

Yes, you can. By chaining `list_sales_history` and `list_commissions`, you can build a logic block that identifies discrepancies between expected and actual payouts.

You inject your credentials via environment variables during the server initialization. The client then handles authentication headers for every request made by your agents.

It does. Because the tools integrate directly with the framework, every input and output is captured in your traces, allowing you to debug your agent's reasoning.

You define the logic within your agent's system prompt. The agent decides how to parse the JSON returned by tools like `list_products` to extract exactly what you need.

Your Hotmart transaction data is only accessed through authenticated tool calls. We ensure that sensitive customer information, specifically email addresses found in `get_sale_by_user`, remains ephemeral and is never stored by the server itself.

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