How to Use the AfterShip MCP in LlamaIndex

Q: How can I use LlamaIndex to find all my late AfterShip shipments?

Set up a LlamaIndex agent to periodically call AfterShip's listtrackings tool and index the results into a vector store you control. After that, you can ask your RAG app, "which shipments are delayed?" It will query the indexed data to give you a list.

Q: Can I search for shipments by an internal project code?

Yes. When you call createtracking, include your project code in the customfields. LlamaIndex will index this along with all the other shipment data, making it fully searchable with natural language.

Q: How do I start with the AfterShip MCP server and LlamaIndex?

Install the llama-index-tools-mcp package. Then, you point the BasicMCPClient at your Vinkius endpoint and wrap it in the McpToolSpec. That's it. Your agent now has access to the full set of AfterShip tools.

Turn your real-time AfterShip data into a queryable knowledge base with LlamaIndex. Index MCP tool output and ask questions in plain English.

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Connect AfterShip MCP to LlamaIndex

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Setup AfterShip with LlamaIndex

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Index Live Shipment Data

LlamaIndex doesn't just call AfterShip tools; it intelligently indexes their output. When your agent runs `list_trackings`, the results—every status, destination, and tracking number—are automatically vectorized and stored. This builds a searchable history of all your shipment operations. You're creating a private, semantic index of your logistics data. The MCP tool is the source, and LlamaIndex makes its output part of your agent's long-term memory.

Ask Questions About Your Shipments

Once your data is indexed, you can ask real questions. "Show me all packages stuck in customs in Germany" is a valid query. LlamaIndex searches the data it previously indexed from `list_trackings` to find the answer. This is RAG (Retrieval-Augmented Generation) in action. Your agent's answers are grounded in actual data from AfterShip, not just its base training. It's the difference between guessing and knowing.

Your AfterShip MCP Server as a Data Source

Use the `McpToolSpec` to treat the AfterShip MCP Server like any other native data loader in LlamaIndex. This lets your RAG application combine live shipping data with your other knowledge sources, like PDFs, Notion, or a database. Your agent can now answer questions by pulling information from a product manual and cross-referencing it with live delivery status from the `get_tracking` tool. It unifies your static documents and dynamic API data.

Setup guide

Set up AfterShip MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all AfterShip MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to AfterShip tools.",
)
response = await agent.run("List recent AfterShip data")

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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. 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 MCP in LlamaIndex

Set up a LlamaIndex agent to periodically call AfterShip's `list_trackings` tool and index the results into a vector store you control. After that, you can ask your RAG app, "which shipments are delayed?" It will query the indexed data to give you a list.

Yes. When you call `create_tracking`, include your project code in the `custom_fields`. LlamaIndex will index this along with all the other shipment data, making it fully searchable with natural language.

No. It first queries the local index of data it has already fetched. If the answer is there, it's fast and requires no API call. It only calls live AfterShip tools like `get_tracking` when it needs fresh data or to perform an action.

Install the `llama-index-tools-mcp` package. Then, you point the `BasicMCPClient` at your Vinkius endpoint and wrap it in the `McpToolSpec`. That's it. Your agent now has access to the full set of AfterShip tools.

Your LlamaIndex application processes shipment details like tracking numbers, delivery statuses, and courier slugs. LlamaIndex then indexes this data into a vector store database that you manage. Vinkius infrastructure only handles the tool execution itself and does not persist your data.

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