How do I award points for a specific activity?

Use the `award_points_activity` tool with the member's email and the specific `activity_id` defined in your Zinrelo dashboard.

Can I see a history of point redemptions for a user?

Yes, the `list_member_transactions` tool retrieves a complete history of all point earnings and redemptions for a target member.

Is it possible to manually deduct points?

Absolutely. Use the `deduct_points` tool to remove a specific amount of points from a user's loyalty balance.

Zinrelo MCP Server for LangChain 9 tools — connect in under 2 minutes

Name: Zinrelo
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 9 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Zinrelo through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "zinrelo": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Zinrelo, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Zinrelo MCP Server

Connect your Zinrelo account to any AI agent to automate your loyalty and rewards operations. This MCP server enables your agent to interact with loyalty members, award points for activities or purchases, and manage reward redemptions directly from natural language.

LangChain's ecosystem of 500+ components combines seamlessly with Zinrelo through native MCP adapters. Connect 9 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Member Management — Enroll new customers and retrieve detailed loyalty profiles, including tier status and point balances
Points Automation — Award points for custom activities or purchase transactions instantly
Reward Processing — Redeem points for rewards and manage manual point deductions when necessary
Activity Auditing — List comprehensive transaction histories for any loyalty member to track earnings and usage
Program Oversight — Access high-level loyalty settings and account configuration details

The Zinrelo MCP Server exposes 9 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Zinrelo to LangChain via MCP

Follow these steps to integrate the Zinrelo MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 9 tools from Zinrelo via MCP

Why Use LangChain with the Zinrelo MCP Server

LangChain provides unique advantages when paired with Zinrelo through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Zinrelo MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Zinrelo queries for multi-turn workflows

Zinrelo + LangChain Use Cases

Practical scenarios where LangChain combined with the Zinrelo MCP Server delivers measurable value.

RAG with live data: combine Zinrelo tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Zinrelo, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Zinrelo tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Zinrelo tool call, measure latency, and optimize your agent's performance

Zinrelo MCP Tools for LangChain (9)

These 9 tools become available when you connect Zinrelo to LangChain via MCP:

award_points_activity

Award points for a custom activity

award_points_purchase

Award points for a purchase

deduct_points

Manually deduct points from a user

enroll_member

Enroll or update a loyalty member

get_loyalty_settings

Get account loyalty settings

get_member_details

Get details for a specific loyalty member

list_loyalty_members

List all loyalty program members

list_member_transactions

List transaction history for a member

redeem_reward

g., coupon). Redeem points for a reward

Example Prompts for Zinrelo in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Zinrelo immediately.

"Show me the loyalty profile for 'customer@example.com'."

"Award 500 points to 'jane.doe@example.com' for a $50.00 purchase."

"List all transactions for 'john.smith@example.com'."

Troubleshooting Zinrelo MCP Server with LangChain

Common issues when connecting Zinrelo to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Zinrelo + LangChain FAQ

Common questions about integrating Zinrelo MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.