Can I track packages from different carriers like DHL and FedEx at once?

Yes! 17Track supports over 1,500 carriers. You can register any tracking number, and the `detect_carrier` tool can help identify which provider is handling the shipment.

How do I add a friendly name to a tracking number?

Use the `update_tracking_tag` tool and provide the tracking number along with your desired text in the `tag` parameter. This makes it easier to identify shipments later.

What happens if I don't know the carrier ID?

No problem. You can use the `detect_carrier` tool with the tracking number first. It will return the most likely carrier IDs, which you can then use to register the shipment.

17Track MCP Server for Pydantic AI 7 tools — connect in under 2 minutes

Name: 17Track
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect 17Track through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to 17Track "
            "(7 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in 17Track?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About 17Track MCP Server

Equip your AI agent with the most comprehensive logistics intelligence available via 17Track. This unified server provides your agent with instant access to real-time shipment status, event history, and carrier metadata for over 1,500 global logistics providers. Your agent can instantly register new tracking numbers, audit shipping progress, and retrieve detailed event logs without you ever checking a tracking page. Whether you are managing e-commerce fulfillment or tracking personal orders, your agent acts as a dedicated logistics coordinator through natural conversation.

Pydantic AI validates every 17Track tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Shipment Monitoring — Register and track thousands of packages simultaneously with real-time status updates.
Event Auditing — Fetch complete historical logs and specific milestone events for any tracking number.
Carrier Intelligence — Automatically detect the carrier for a given number and list all supported global providers.
Metadata Management — Add tags and names to your shipments to keep your logistics organized.
Inventory Control — Stop or delete tracking for completed shipments to maintain a clean dashboard.

The 17Track MCP Server exposes 7 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect 17Track to Pydantic AI via MCP

Follow these steps to integrate the 17Track MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from 17Track with type-safe schemas

Why Use Pydantic AI with the 17Track MCP Server

Pydantic AI provides unique advantages when paired with 17Track through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your 17Track integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your 17Track connection logic from agent behavior for testable, maintainable code

17Track + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the 17Track MCP Server delivers measurable value.

Type-safe data pipelines: query 17Track with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple 17Track tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query 17Track and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock 17Track responses and write comprehensive agent tests

17Track MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect 17Track to Pydantic AI via MCP:

delete_tracking

Delete a tracking number

detect_carrier

Detect carrier for a number

get_tracking_info

Get status for a tracking number

list_carriers

List all supported carriers

register_tracking

stop_tracking

Stop tracking a number

update_tracking_tag

Update tracking metadata

Example Prompts for 17Track in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with 17Track immediately.

"Register tracking number '123456789' for my order."

"Get the latest status for my package '123456789'."

"Detect which carrier is handling tracking number 'XY123456789Z'."

Troubleshooting 17Track MCP Server with Pydantic AI

Common issues when connecting 17Track to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

17Track + Pydantic AI FAQ

Common questions about integrating 17Track MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your 17Track MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.