AfterShip MCP Server for LangChain 9 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect AfterShip through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"aftership": {
"transport": "streamable_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,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using AfterShip, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 AfterShip MCP Server
Connect AfterShip tracking platform to any AI agent and track packages from 1,000+ couriers worldwide, auto-detect shipping companies, and manage all your shipments through natural language.
LangChain's ecosystem of 500+ components combines seamlessly with AfterShip through native MCP adapters. Connect 9 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Package Tracking — Create and monitor shipments from FedEx, UPS, DHL, USPS, and 1,000+ other couriers
- Auto-Detect Courier — Automatically identify the shipping company from just a tracking number
- Tracking History — View complete delivery history with checkpoint timestamps and locations
- Delivery Management — Mark trackings as completed, retrack expired ones, or delete old entries
- Customer Notifications — Set up email and SMS notifications for delivery updates
- Courier Directory — Browse all supported courier companies with their contact info and requirements
The AfterShip MCP Server exposes 9 tools through the Vinkius. Connect it to LangChain 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 to LangChain via MCP
Follow these steps to integrate the AfterShip MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 9 tools from AfterShip via MCP
Why Use LangChain with the AfterShip MCP Server
LangChain provides unique advantages when paired with AfterShip through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine AfterShip MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across AfterShip queries for multi-turn workflows
AfterShip + LangChain Use Cases
Practical scenarios where LangChain combined with the AfterShip MCP Server delivers measurable value.
RAG with live data: combine AfterShip tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query AfterShip, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain AfterShip tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every AfterShip tool call, measure latency, and optimize your agent's performance
AfterShip MCP Tools for LangChain (9)
These 9 tools become available when you connect AfterShip to LangChain via MCP:
create_tracking
Requires at least the tracking number. Optionally specify the courier slug, title, customer emails, SMS phone numbers, order ID, and custom fields. Create a new package tracking
delete_tracking
This action cannot be undone. Delete a tracking entry
detect_courier
Useful when the user provides a tracking number but doesn't know which courier it belongs to. Returns a ranked list of likely couriers. Auto-detect courier from tracking number
get_tracking
Get details of a specific tracking
list_couriers
) that can be used for tracking packages. List all supported courier companies
list_trackings
Supports extensive filtering by courier (slug), tag, keyword, origin, destination, date ranges, and delivery status. List all package trackings
mark_tracking_completed
Useful when the package has been delivered but the courier hasn't updated the final status. Mark a tracking as completed
retrack_tracking
This restarts monitoring and will fetch new checkpoint updates. Retrack an expired tracking
update_tracking
Does not affect the tracking number or courier. Update an existing tracking
Example Prompts for AfterShip in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with AfterShip immediately.
"Track my package with tracking number 1Z999AA10123456784."
"What courier handles tracking number 9400111899223344556677?"
"Show me all my active trackings."
Troubleshooting AfterShip MCP Server with LangChain
Common issues when connecting AfterShip to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersAfterShip + LangChain FAQ
Common questions about integrating AfterShip MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect AfterShip with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect AfterShip to LangChain
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.