How to Use the 17Track MCP in Pydantic AI

Q: Can I use 17Track to detect carriers in Pydantic AI?

Your agent calls detectcarrier to verify the provider for any number. The result is validated against your schema before the agent acts on it.

Type-safe shipment tracking for Pydantic AI workflows.

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Connect 17Track MCP to Pydantic AI

Create your Vinkius account to connect 17Track 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 17Track with Pydantic AI

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Validate tracking data in Pydantic AI

The `get_tracking_info` tool returns data that your agent validates against strict Pydantic models. If the carrier returns malformed data, your agent catches it immediately. You never deal with silent failures or corrupted shipment updates. The server ensures the response schema matches your expectations every single time.

Register shipments with Pydantic AI

Your agent uses `register_tracking` to input new numbers into the system. Since you use Pydantic AI, every registration request is type-checked before it leaves your local environment. This prevents bad input from reaching the tracking platform. You maintain a clean source of truth for all your active parcels.

Clean up tracking data

Remove finished shipments using `delete_tracking` or `stop_tracking`. These operations ensure your tracking registry stays current and accurate. Use `update_tracking_tag` to rename or categorize your shipments. Your agent maintains perfect state synchronization with the underlying tracking platform.

Setup guide

Set up 17Track 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": {
        "17track-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to 17Track tools.",
)

result = await agent.run("List recent 17Track transactions")
print(result.output)

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Common questions about 17Track MCP in Pydantic AI

You instantiate an MCPToolset with the server URL and add it to your agent's toolsets list. It handles all communications through the Streamable HTTP protocol.

It does. Because Pydantic AI validates every response, you get runtime protection against unexpected API changes from the carrier.

Your agent calls `detect_carrier` to verify the provider for any number. The result is validated against your schema before the agent acts on it.

The server supports SSE and Streamable HTTP. You define the transport method when setting up the toolset connection.

The server only consumes the tracking numbers sent by your agent. No persistent storage of your shipment information occurs on the MCP server itself.

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