How to Use the DCL Logistics MCP in Pydantic AI

Use type-safe DCL Logistics tools with Pydantic AI to ensure your agents never hallucinate fulfillment data.

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Connect DCL Logistics MCP to Pydantic AI

Create your Vinkius account to connect DCL Logistics 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 DCL Logistics with Pydantic AI

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Validate DCL Logistics data in Pydantic AI

Every response from `get_account_details` is checked against Pydantic models to ensure your agent receives exactly what it expects. If the API schema shifts, the agent stops instead of processing bad data. It forces your code to handle missing fields or unexpected types safely. You get total control over the data coming into your agent's brain.

Manage DCL Logistics returns with Pydantic AI

Use `list_customer_returns` to track RMAs with full type safety. The agent validates each return reason and credit status against your defined models before acting. It ensures your fulfillment logic only runs on verified records. No more silent failures or corrupted state during return processing.

Audit DCL Logistics inventory using Pydantic AI

Tools like `get_sku_inventory_status` return strictly typed inventory objects for your agent to analyze. You define the Pydantic structure and the tool populates it. It makes your inventory checks predictable and easy to debug. If the data doesn't match your model, your agent raises a clear validation error immediately.

Setup guide

Set up DCL Logistics 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": {
        "dcl-logistics-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about DCL Logistics MCP in Pydantic AI

The MCP tools return JSON that gets cast into your Pydantic models at runtime. If the structure fails, the agent reports a validation error, preventing bad data usage.

Yes, the server supports SSE and Streamable HTTP. Pydantic AI works with both, letting you choose the transport that fits your deployment.

The tools are model-agnostic. As long as you use Pydantic AI to define your models, you can switch between any LLM provider without changing your tool logic.

Just define your MCPToolset with the server URL. Pydantic AI handles the tool registration and type mapping for you.

Your inventory and order data are processed through a zero-trust architecture. Only your authorized agent can request records, and all traffic remains encrypted.

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