How to Use the Coppel MCP in Pydantic AI

Q: Can I use Pydantic AI to filter Coppel store locations?

Yes. Your agent can call liststores and filter results by Mexican states. The returned store data is validated against a strict schema so you can parse addresses safely.

Q: How does the agent track weekly payment schedules?

The agent uses listpayments to fetch installment histories. Each payment record, including dates and receipt references, is validated to ensure accurate financial reporting.

Guarantee type-safe Coppel retail operations and credit checks with runtime validation using Pydantic AI and our secure MCP Server.

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Connect Coppel MCP to Pydantic AI

Create your Vinkius account to connect Coppel 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 Coppel with Pydantic AI

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Type-safe Coppel order creation with Pydantic AI

The `create_order` tool processes retail transactions by validating product IDs, quantities, and delivery addresses against strict runtime schemas. Your Pydantic AI agent catches formatting errors before they hit the live API, preventing costly ordering mistakes. If a field is missing or formatted incorrectly, the framework throws a validation error immediately. This prevents corrupted data from entering the Mexican retail pipeline, ensuring every transaction is structured perfectly.

Validate customer credit profiles at runtime

The `get_customer` tool pulls detailed customer profiles, which are instantly parsed into typed Python models. This MCP server validates phone numbers, addresses, and credit IDs to match your application's expected formats. To check financing options, the agent runs `get_customer_credit` to inspect active credit lines. This ensures your agent only offers weekly payment plans (abonos) to customers who meet your strict validation rules.

Query catalogs with guaranteed schemas

The `search_products` tool returns matching items with precise pricing and credit plan availability. Your Pydantic AI agent parses this data into clean Python objects, giving your application guaranteed access to catalog details. You can also list categories using `list_categories` to build dynamic, type-safe menus. The agent handles the raw JSON parsing under the hood, so your code never runs into unhandled key errors.

Setup guide

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

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

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

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Common questions about Coppel MCP in Pydantic AI

Use the MCPToolset class pointed at your Vinkius HTTP endpoint and pass it to your agent. This unified approach handles the connection and validates all tool outputs against strict schemas.

The framework raises a validation error instantly instead of letting your agent hallucinate. This keeps your retail database clean and prevents silent failures in production.

Yes. Your agent can call `list_stores` and filter results by Mexican states. The returned store data is validated against a strict schema so you can parse addresses safely.

The agent uses `list_payments` to fetch installment histories. Each payment record, including dates and receipt references, is validated to ensure accurate financial reporting.

Vinkius processes all requests inside an isolated V8 sandbox with zero persistent storage. Your customer credit scores, payment history, and credit limits are processed in memory and deleted the moment the API call completes.

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