How to Use the Fidelizador MCP in LangChain

Q: How do I connect LangChain to the Fidelizador MCP Server?

Install langchain-mcp-adapters and langgraph first. Initialize the MultiServerMCPClient with the Vinkius URL, then pass the tools from client.gettools() directly into your LangChain agent.

Q: Can a LangChain agent manage lists using Fidelizador automatically?

Yes, the agent uses tools like listmailinglists and createmailinglist to organize your shoppers. It evaluates user behavior and updates the lists without manual intervention.

Q: How do I debug failed Fidelizador tool calls in LangChain?

Use LangSmith to trace the execution of tools like updatecontact or getcampaign. You will see the exact inputs, outputs, and any API errors returned by the server.

Q: Does this integration support persistent context across sessions?

It is stateless by default, but you can use client.session() to keep context active. This helps your agent remember which campaign it pulled via listcampaigns during a long conversation.

Run multi-step loyalty campaigns with LangChain and Fidelizador to keep your shoppers active.

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Connect Fidelizador MCP to LangChain

Create your Vinkius account to connect Fidelizador 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 Fidelizador with LangChain

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Chain loyalty actions with LangChain and this MCP Server

The `list_mailing_lists` tool lets your LangChain agent audit active segments before triggering any marketing actions. It can then grab campaign details with `get_campaign` and register a new shopper via `create_contact` in a single run. The output of one tool feeds directly into the next without you writing glue code. This means your agent can independently decide when to trigger `update_contact` to adjust points based on how a customer interacted with your latest campaign.

Trace every campaign mutation in LangSmith

Tracking loyalty points requires precision, which is why `update_contact` logs every change directly inside LangSmith. When your LangChain agent executes a tool, you see the exact payload, latency, and token cost in your dashboard. If a customer complains about missing points, you don't have to dig through raw server logs. You check the trace for `get_campaign` to see if the reward logic matched the customer's profile before the chain stopped.

Connect loyalty data to 500+ LangChain integrations

Integrating your loyalty program with external databases starts with `create_mailing_list` to segment incoming users. Combine this MCP Server with your database or vector store using LangChain's massive ecosystem to enrich your data. Your agent can pull purchase history from your SQL database and run a reasoning step. After that, it calls `create_mailing_list` or `delete_contact` to keep your marketing segments clean.

Setup guide

Set up Fidelizador 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 Fidelizador 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({
    "fidelizador-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 Fidelizador 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 Fidelizador. 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 Fidelizador MCP in LangChain

Install `langchain-mcp-adapters` and `langgraph` first. Initialize the `MultiServerMCPClient` with the Vinkius URL, then pass the tools from `client.get_tools()` directly into your LangChain agent.

Yes, the agent uses tools like `list_mailing_lists` and `create_mailing_list` to organize your shoppers. It evaluates user behavior and updates the lists without manual intervention.

Use LangSmith to trace the execution of tools like `update_contact` or `get_campaign`. You will see the exact inputs, outputs, and any API errors returned by the server.

It is stateless by default, but you can use `client.session()` to keep context active. This helps your agent remember which campaign it pulled via `list_campaigns` during a long conversation.

Vinkius runs this server inside an isolated sandbox, keeping your contact details and mailing lists safe. Your API tokens are managed securely, so your LangChain agent never exposes credentials when updating loyalty points.

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