AfterShip MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AfterShip as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to AfterShip. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in AfterShip?"
)
print(response)
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.
LlamaIndex agents combine AfterShip tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the AfterShip MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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 9 tools from AfterShip
Why Use LlamaIndex with the AfterShip MCP Server
LlamaIndex provides unique advantages when paired with AfterShip through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AfterShip tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AfterShip tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AfterShip, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AfterShip tools were called, what data was returned, and how it influenced the final answer
AfterShip + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AfterShip MCP Server delivers measurable value.
Hybrid search: combine AfterShip real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AfterShip to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying AfterShip for fresh data
Analytical workflows: chain AfterShip queries with LlamaIndex's data connectors to build multi-source analytical reports
AfterShip MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect AfterShip to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting AfterShip to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAfterShip + LlamaIndex FAQ
Common questions about integrating AfterShip MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.