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 Pydantic AI 9 tools — connect in under 2 minutes

Name: Zinrelo
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 9 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Zinrelo through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Zinrelo "
            "(9 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Zinrelo?"
    )
    print(result.data)

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.

Pydantic AI validates every Zinrelo tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Zinrelo MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 9 tools from Zinrelo with type-safe schemas

Why Use Pydantic AI with the Zinrelo MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Zinrelo integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Zinrelo connection logic from agent behavior for testable, maintainable code

Zinrelo + Pydantic AI Use Cases

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

Type-safe data pipelines: query Zinrelo with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Zinrelo tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Zinrelo and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Zinrelo responses and write comprehensive agent tests

Zinrelo MCP Tools for Pydantic AI (9)

These 9 tools become available when you connect Zinrelo to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Zinrelo + Pydantic AI FAQ

Common questions about integrating Zinrelo MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Zinrelo MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.