How to Use the Ship24 MCP in LlamaIndex
Build knowledge-augmented RAG applications with LlamaIndex.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Ship24 MCP to LlamaIndex
Create your Vinkius account to connect Ship24 to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Key Capabilities
Indexing Tracking History via LlamaIndex
When you run a tracking job, the output becomes part of your index. The client can use `get_tracking_results` to pull status updates which get saved into your vector store. You can then query past sessions and ask questions like 'Why did shipment X fail on Tuesday?' This means your AI agent doesn't just call a tool; it builds institutional knowledge from the API data.
Bulk Data Ingestion with LlamaIndex
Need to track 50 shipments and remember every detail? Use `create_bulk_trackers`. The results are indexed, giving you a searchable record of all those items. Later, your agent can retrieve specific details using `list_bulk_trackers` from the knowledge base. This is perfect for analyzing large batches of logistics data.
Understanding Carrier Capabilities with LlamaIndex
Before running a job, your agent can use `search_couriers` to check support details for specific carriers like FedEx or Correios. These successful searches are indexed. If you have multiple servers feeding into one index, the MCP Server provides consistent data. It helps ground answers in verifiable API facts.
Set up Ship24 MCP in LlamaIndex
Prerequisites
- Python 3.10+ installed
-
llama-index-tools-mcppackage - 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 providesBasicMCPClientandMcpToolSpec. - 2
Connect with BasicMCPClient
Point
BasicMCPClientto 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 Ship24 MCP tools into nativeFunctionToolobjects that any LlamaIndex agent can use. - 4
Run with any LLM
Create a
FunctionAgentwith the tools and your preferred LLM. SwapOpenAIforAnthropic,Gemini, or any LlamaIndex-supported provider.
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 Ship24 tools.",
)
response = await agent.run("List recent Ship24 data") Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Ship24. 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 Ship24 MCP in LlamaIndex
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