How to Use the Hotmart MCP in LangChain

Q: Can LangChain agents aggregate Hotmart sales data?

Yes. Your agent can call getsalessummary and combine the output with your other database tools in a single chain. It handles the math and formatting automatically.

Q: Why use LangChain for Hotmart student tracking?

You get full observability. When you run getstudentprogress, LangSmith logs the exact latency and token usage. You see exactly how the agent reached its conclusions.

Connect your LangChain agents directly to the Hotmart MCP Server to build multi-step sales and student progress pipelines.

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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

Build automated sales reporting chains

The `get_sales_summary` tool feeds directly into your LangChain analytics pipelines. You set up a ReAct agent that pulls daily revenue numbers, formats the data, and passes it to your internal dashboard. No manual CSV exports required. When you need deeper context, the agent triggers `get_sales_history`. It parses individual transaction records to identify refund patterns or upsell opportunities. LangSmith traces the entire execution so you know exactly which inputs generated the final report.

LangChain agents track student drop-offs

The `get_student_progress` tool lets your agent monitor exactly where users stop watching your courses. You string this together with `list_students` to build a chain that identifies at-risk accounts before they cancel. Your multi-step pipeline takes that output and feeds it into a messaging node. The agent decides which students need a check-in email and which ones are doing fine. You just review the final action logs.

Hotmart MCP Server subscription routing

The `list_subscriptions` tool exposes active recurring revenue directly to your LangChain setup. You build a node that checks subscription status before granting access to community features. Combine this with `list_products` to map exactly what each member bought. Your agent uses these two tools to verify access rights across your entire digital catalog. It runs in the background, keeping your external systems in sync.

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-alternative-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

Install the langchain-mcp-adapters package. Initialize MultiServerMCPClient with your endpoint URL and pass the tools to your ReAct agent.

Yes. Your agent can call get_sales_summary and combine the output with your other database tools in a single chain. It handles the math and formatting automatically.

You get full observability. When you run get_student_progress, LangSmith logs the exact latency and token usage. You see exactly how the agent reached its conclusions.

LangChain is stateless by default. You need to use client.session() if you want the agent to remember past queries across multiple turns.

The V8 Isolate Sandbox ensures isolated execution. When your agent pulls transaction records via get_sales_history, the data stays within an ephemeral memory space. No external logging occurs outside your configured LangSmith environment.

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