How to Use the Fidelizador MCP in Pydantic AI

Build bulletproof loyalty workflows with Pydantic AI, enforcing strict type safety on all your Fidelizador operations.

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Connect Fidelizador MCP to Pydantic AI

Create your Vinkius account to connect Fidelizador 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 Fidelizador with Pydantic AI

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Strict type validation for your MCP Server

Never worry about your Pydantic AI agent passing malformed data to your Fidelizador loyalty database. When you use Pydantic AI, every payload sent to Fidelizador's `create_contact` and `update_contact` is validated against strict Python schemas at runtime. If the Pydantic AI agent passes a string instead of an integer loyalty score, the framework halts execution before hitting Fidelizador. This strict validation prevents Fidelizador database pollution and silent Pydantic AI errors. Your system catches issues during development, ensuring only clean customer profiles reach Fidelizador.

Validate mailing lists before sending campaigns

Manage your Fidelizador marketing segments with absolute confidence using Pydantic AI. Pydantic AI validates the structure of lists returned by Fidelizador's `list_mailing_lists` and `create_mailing_list`. If you run automated scripts to clean contacts, Pydantic AI guarantees that calls to `delete_contact` are executed with precise Fidelizador identifiers. You get total control over your loyalty workflows without sacrificing speed.

Type-safe tracking of campaign performance

Analyze your Fidelizador marketing metrics in Pydantic AI without fearing unexpected API changes. Pydantic AI forces your agent to parse the outputs of `list_campaigns` and `get_campaign` into structured Python models, making sure your reporting scripts never break due to unexpected null values. This setup is perfect for retail systems where Fidelizador campaign data drives Pydantic AI decision loops. Your agent can safely query `list_contacts` to find active Fidelizador buyers, knowing that every field matches your Pydantic AI model.

Setup guide

Set up Fidelizador 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": {
        "fidelizador-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Fidelizador MCP in Pydantic AI

If the server returns data that doesn't match your Pydantic models, the framework raises a validation error instantly. This prevents your agent from processing corrupted contact or campaign records.

Yes, you can define custom schemas that match your Fidelizador database fields. The agent will validate the customer's email, phone, and loyalty points before sending the payload.

Yes, the framework supports SSE and Streamable HTTP transports. You can stream lists of campaigns or contacts while maintaining strict type-safety checks on each incoming chunk.

Use the unified MCPToolset class to point to your Vinkius endpoint. Pass this toolset directly into your Agent's toolsets parameter to expose the loyalty functions to your model.

Data is processed through an ephemeral, zero-trust V8 sandbox on Vinkius. Your customer profiles, email addresses, and list details are never stored locally or used to train external models.

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