How to Use the Zinrelo MCP in Pydantic AI

Ensure perfect data integrity across loyalty programs using Pydantic AI with Zinrelo MCP Server.

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Connect Zinrelo MCP to Pydantic AI

Create your Vinkius account to connect Zinrelo to Pydantic AI 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 Pydantic AI

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Execute Loyalty Actions via MCP Server

Use the MCP Server to manage point flow. Whether awarding points for a purchase (`award_points_purchase`) or deducting them manually (`deduct_points`), every piece of data is validated against Pydantic models at runtime. This eliminates silent corruption. The agent handles new member onboarding with `enroll_member`, guaranteeing that all required fields are present and correctly typed upon execution.

Retrieve And Validate Member Details

Need a user's profile? The agent can fetch details using `get_member_details`. When it calls this tool, Pydantic validates the response structure. You won't get hallucinated or unexpected fields. Check transaction history with `list_member_transactions`, knowing that every returned record conforms exactly to your defined Python types.

Control Rewards and Program Settings

The server allows the agent to manage rewards using `redeem_reward`. Furthermore, you can check program rules by calling `get_loyalty_settings`. If an API response for these settings is malformed, Pydantic fails loudly—you know instantly if data integrity is broken. This strict validation makes it reliable for mission-critical actions like awarding points for activities (`award_points_activity`).

Setup guide

Set up Zinrelo MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "zinrelo-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Zinrelo tools.",
)

result = await agent.run("List recent Zinrelo transactions")
print(result.output)

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Common questions about Zinrelo MCP in Pydantic AI

Pydantic validates every response from the MCP Server. When your agent calls a tool like `list_loyalty_members`, the resulting data is immediately checked against your Python schema for guaranteed correctness.

It absolutely does. If the backend API returns a field that shouldn't be there, or if it changes data types unexpectedly, your agent fails with a clear validation error, preventing any bad code execution.

Yes. Because the focus is on runtime type safety, you gain confidence that the data being passed to your downstream systems—whether it's member details or point balances—is 100% correct.

The agent doesn't proceed until the data passes validation. You get an immediate, specific error message indicating *why* the schema was violated, which is far better than silent failure.

This server manages account attributes, specifically point totals, member IDs, and transaction records. The primary benefit here is guaranteeing the structure of this sensitive membership data.

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