How to Use the Zinrelo MCP in LangChain

Q: Can I use my AI client to check loyalty program rules with Zinrelo and LangChain?

Yes. You start by calling getloyaltysettings. Your ReAct agent interprets the returned data, allowing it to make subsequent decisions about awarding points or enrolling a new member.

Q: Does the Zinrelo MCP Server support point deductions in LangChain?

Absolutely. The deductpoints tool allows for manual deduction. Your agent can incorporate this into its reasoning pipeline, ensuring that financial actions are always intentional and traceable.

Build multi-step loyalty workflows using LangChain and Zinrelo MCP Server.

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Connect Zinrelo MCP to LangChain

Create your Vinkius account to connect Zinrelo 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 Zinrelo with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automating Member Lifecycle Management

You can build agent chains that manage the entire member journey. An agent decides when to call `enroll_member` or if a user needs updating via `get_member_details`. The output of one tool, like checking existing status, feeds directly into the next step. This makes complex flows possible. For instance, after calling `award_points_purchase`, you can immediately follow up by listing all members with `list_loyalty_members` to confirm the point update across the whole database.

Complex Point Transaction Pipelines

The system handles multi-step financial logic perfectly. An agent needs to check current settings using `get_loyalty_settings`, then deduct points manually via `deduct_points`. The ReAct framework ensures the deduction only happens if the initial check passes. This chaining capability is key for accurate reporting. You can sequence a reward redemption (`redeem_reward`) followed by a transaction log retrieval using `list_member_transactions` to create an auditable record.

Dynamic Program Configuration Checks

Need to know what rules apply before running code? Your agent can first call `get_loyalty_settings`. This provides the context needed for subsequent actions, such as deciding if a point award should use `award_points_activity` or `award_points_purchase`. Use this sequence when building complex business logic. You get the settings data, pass it to another tool call, and then proceed with enrollment using `list_loyalty_members`, all in one observable chain.

Setup guide

Set up Zinrelo 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 Zinrelo 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({
    "zinrelo-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 Zinrelo transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Zinrelo MCP in LangChain

LangSmith handles the observability. It traces every tool input and output across your entire agent chain, showing exactly when `deduct_points` was called and what data it used.

You simply pass the member ID obtained from a preceding step into the `list_member_transactions` tool. The agent decides when that data is necessary for its overall goal.

Yes. You start by calling `get_loyalty_settings`. Your ReAct agent interprets the returned data, allowing it to make subsequent decisions about awarding points or enrolling a new member.

Absolutely. The `deduct_points` tool allows for manual deduction. Your agent can incorporate this into its reasoning pipeline, ensuring that financial actions are always intentional and traceable.

This server handles structured customer loyalty data, including member IDs, point totals, and transaction history. It's all managed within the context of your AI client.

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