How do I find my AfterShip API Key?

Log in to your AfterShip account, navigate to **Settings** > **API Keys**. You can generate a new API key there. This key works for both Returns and Tracking APIs.

Can I approve a return request via the agent?

Yes! Use the `approve_return` tool and provide the Return ID. Your agent will update the status in AfterShip, which can also trigger automatic label generation if configured.

Does this support marking items as received?

Yes, you can use the `receive_items` tool to record which items from an RMA have arrived at your warehouse and their current condition grade.

AfterShip Returns MCP Server for Pydantic AI 4 tools — connect in under 2 minutes

Name: AfterShip Returns
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 4 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect AfterShip Returns 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 AfterShip Returns "
            "(4 tools)."
        ),
    )

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

asyncio.run(main())

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About AfterShip Returns MCP Server

Connect your AfterShip Returns account to your AI agent to unlock professional returns management and customer experience orchestration. From auditing pending return requests to approving RMAs and generating shipping labels, your agent handles your reverse logistics through natural conversation.

Pydantic AI validates every AfterShip Returns tool response against typed schemas, catching data inconsistencies at build time. Connect 4 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

Return Request Management — List and audit return requests from customers and check their approval status
RMA Orchestration — Retrieve detailed technical metadata for specific RMAs, including item details and reasons for return
Label Generation Support — Monitor shipment creation and retrieve tracking information for return packages
Logistics Oversight — Mark items as received and grade their condition to streamline your warehouse workflow
Process Insights — Quickly identify common return reasons or identify bottlenecks in your return policy directly from chat

The AfterShip Returns MCP Server exposes 4 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 AfterShip Returns to Pydantic AI via MCP

Follow these steps to integrate the AfterShip Returns 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 4 tools from AfterShip Returns with type-safe schemas

Why Use Pydantic AI with the AfterShip Returns MCP Server

Pydantic AI provides unique advantages when paired with AfterShip Returns 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 AfterShip Returns integration code

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

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

AfterShip Returns + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the AfterShip Returns MCP Server delivers measurable value.

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

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

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

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

AfterShip Returns MCP Tools for Pydantic AI (4)

These 4 tools become available when you connect AfterShip Returns to Pydantic AI via MCP:

approve_return

This allows the customer to ship the item back. Authorize a pending return request to immediately trigger generating the return shipping label

get_return_details

Retrieve the granular items, return reasons, and current logistics status for a specific RMA

list_returns

Retrieve pending or historical customer return requests and their processing statuses

receive_items

Record the arrival and physical grading condition of returned items arriving at the warehouse

Example Prompts for AfterShip Returns in Pydantic AI

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

"List all pending return requests from the last 48 hours."

"Approve return request ID 'ret_abc123'."

"Show me details for RMA number 'RMA-98765'."

Troubleshooting AfterShip Returns MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

AfterShip Returns + Pydantic AI FAQ

Common questions about integrating AfterShip Returns 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 AfterShip Returns MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.