How to Use the AfterShip Returns MCP in LangChain

Q: Can I automate the approval of AfterShip Returns via LangChain?

Yes, just map the approvereturn tool to your agent's decision logic. It fires the label generation as soon as your criteria are met.

Q: How do I handle warehouse receiving for AfterShip Returns in LangChain?

Connect the receiveitems tool to your agent loop. It updates the RMA status once you provide the inspection data from the warehouse.

Chain your AfterShip Returns logic directly into LangChain agents for automated RMA processing.

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Connect AfterShip Returns MCP to LangChain

Create your Vinkius account to connect AfterShip Returns to LangChain 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 AfterShip Returns with LangChain

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ChatGPT

Claude

Perplexity

Trigger labels inside LangChain pipelines

Call `approve_return` to move a pending request into the shipping phase immediately. The agent pushes the return status forward without manual oversight. This MCP Server allows your chain to take the output of one step and feed it into the next. You define the logic once and let the agent handle the flow.

Query RMA data for your LangChain agents

Use `list_returns` to pull active requests into your agent's current context. You get a raw list of pending actions to work through. Run `get_return_details` to inspect specific item conditions or reasons for return. The agent sees exactly what the warehouse needs to know.

Mark warehouse arrivals with LangChain tools

Use `receive_items` to log the physical condition of returned inventory as it hits the dock. Your agents record these updates in real-time. This closes the loop on the return lifecycle. The agent updates your database while you watch the trace in LangSmith.

Setup guide

Set up AfterShip Returns MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes AfterShip Returns tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "aftership-returns-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent AfterShip Returns transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AfterShip Returns. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about AfterShip Returns MCP in LangChain

Install the MCP adapters and point your client to the server URL. Pass the tool list into your agent definition to start processing returns automatically.

Yes, just map the `approve_return` tool to your agent's decision logic. It fires the label generation as soon as your criteria are met.

You get full visibility into every tool call through LangSmith. Every request and response is recorded for debugging your agent's behavior.

Connect the `receive_items` tool to your agent loop. It updates the RMA status once you provide the inspection data from the warehouse.

Vinkius handles your connection through an ephemeral sandbox. Only your specific RMA payloads pass through the encrypted tunnel between the server and your agent.

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