How to Use the ApparelMagic MCP in Pydantic AI

Strongly typed ApparelMagic integrations for Pydantic AI. Force your agents to validate every single inventory count and order detail at runtime.

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Connect ApparelMagic MCP to Pydantic AI

Create your Vinkius account to connect ApparelMagic 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 ApparelMagic with Pydantic AI

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Claude

Perplexity

Type-Safe Stock Checks

The `list_inventory` and `get_product` tools return exact warehouse quantities. Pydantic AI forces every response through your predefined models before the agent ever sees the data. If the ERP returns a string instead of an integer for a stock count, the framework fails loudly. You catch the validation error immediately instead of letting the agent hallucinate a restock order.

Validate Orders via MCP Server

Your agent fetches sales data using `list_orders` and `get_order`. It tracks fulfillment status by pulling transit records with `list_shipments` and `get_shipment`. Every line item matches your Pydantic schema. If a tracking number is missing from the payload, the agent stops the pipeline rather than guessing the package location.

Enforce Pydantic AI Schemas on CRM Data

The `list_customers` and `get_customer` tools extract buyer profiles from the system. You define exactly what fields matter — email, wholesale tier, billing address. You connect this using the MCPToolset class. The agent relies on guaranteed data structures, making it safe to use with any LLM backend you choose.

Setup guide

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

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

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

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Common questions about ApparelMagic MCP in Pydantic AI

Install the slim package with MCP support. Use MCPToolset pointing to the HTTP endpoint and pass it to your agent's toolsets array.

Pydantic AI throws a runtime validation error. The agent halts execution, preventing bad data from corrupting your downstream processes.

Yes. The framework is model-agnostic. As long as the local model supports tool calling, it can interact with the ERP endpoints.

No. You must use the unified MCPToolset approach. The older server classes are deprecated in the current framework version.

The system reads tracking numbers and physical delivery coordinates. Vinkius handles the authorization layer externally, meaning your application code never touches or stores the raw API keys.

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