How to Use the JSONCargo Maritime MCP in LlamaIndex

Index live maritime data into your LlamaIndex RAG pipeline for grounded, accurate answers.

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Connect JSONCargo Maritime MCP to LlamaIndex

Create your Vinkius account to connect JSONCargo Maritime 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.

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Setup JSONCargo Maritime with LlamaIndex

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Perplexity

Convert voyage logs into searchable knowledge

Use `get_current_voyage_details` to pull raw transit data directly into your vector store. LlamaIndex indexes these results so your agent can query past movements with semantic precision. This turns transient API responses into a long-term knowledge base. You stop hallucinating voyage details and start querying facts grounded in real-time data.

Filter ports with semantic search

Run `list_maritime_ports` to populate your index with current global port coordinates. Once indexed, your agent can answer natural language questions about port availability. Your RAG application becomes a living map. You no longer need to manually parse lists because the index handles the retrieval based on your specific query.

Validate vessel data before indexing

Execute `search_maritime_vessels` to verify identifiers before adding them to your knowledge base. This keeps your vector store clean and prevents duplicates from entering your index. Accuracy starts at the fetch stage. By filtering via the tool first, your LlamaIndex agent maintains a high-fidelity record of every vessel it tracks.

Setup guide

Set up JSONCargo Maritime 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 JSONCargo Maritime 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 JSONCargo Maritime tools.",
)
response = await agent.run("List recent JSONCargo Maritime data")

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Common questions about JSONCargo Maritime MCP in LlamaIndex

Use the McpToolSpec to retrieve the tool list and pass the outputs to your vector index. This automates the ingestion of maritime data into your RAG pipeline.

Yes. You can call the tools directly via the FunctionAgent if you only need live status updates instead of a persistent knowledge base.

You must trigger the tool call to fetch fresh data. LlamaIndex refreshes the index based on the intervals you define in your agent loop.

Data resides only in your defined vector store. We use an ephemeral, zero-trust connection to ensure only your agent reaches the maritime endpoints.

The server shares vessel registry info, port coordinates, and transit logs. All data is scoped strictly to these maritime domains.

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