How to Use the Datalastic Maritime MCP in LlamaIndex

Q: How do I set up Datalastic Maritime with LlamaIndex?

Run pip install llama-index-tools-mcp. Create a BasicMCPClient with your Vinkius URL, wrap it in an McpToolSpec, and await the tool list conversion before passing it to your FunctionAgent.

Q: Which tool should I use to find a specific cargo ship?

Start with searchmaritimevessels using the text name. Once you have the unique identifier, you pass it to the status or history tools for deeper tracking.

Q: Does LlamaIndex filter the Datalastic Maritime MCP tools I expose?

You restrict access using the allowedtools parameter. If you only want the agent to see port data, just expose the port search and details tools.

Turn live maritime data into a searchable knowledge base with LlamaIndex. Index vessel specs and port details instantly.

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Connect Datalastic Maritime MCP to LlamaIndex

Create your Vinkius account to connect Datalastic 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 Datalastic Maritime with LlamaIndex

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Index live vessel specifications

LlamaIndex shifts how you handle API responses. Instead of just reading a screen, you call `get_vessel_pro_specs` for a fleet of container ships and dump the technical dimensions straight into a vector store. Now those deadweight metrics and engine types exist as part of your local knowledge base. When a user asks which ships fit a specific cargo load, the engine doesn't guess. It queries the index you just built from the MCP tool output, giving you grounded answers based on actual maritime registry data.

Query LlamaIndex MCP Server data

Location data makes for excellent semantic context. Your application runs `find_vessels_in_radius` around a major choke point like the Suez Canal. The agent takes that list of nearby ships and indexes their current headings and speeds. You combine this live snapshot with historical documents. If you need to know how current congestion compares to last month, the framework cross-references the fresh `get_vessel_status` data with your stored reports to generate an accurate summary.

Build a searchable port database

Stop manually cross-referencing harbor charts. Your agent loops through `search_ports_by_country` and pulls `get_port_details` for every terminal in Brazil. That raw JSON gets chunked and embedded directly into your RAG pipeline. The next time a logistics planner asks about draft limits in Santos, the system retrieves the exact coordinates and specs. It pulls the answer from the indexed API data rather than relying on outdated LLM training weights.

Setup guide

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

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Common questions about Datalastic Maritime MCP in LlamaIndex

Run `pip install llama-index-tools-mcp`. Create a `BasicMCPClient` with your Vinkius URL, wrap it in an `McpToolSpec`, and await the tool list conversion before passing it to your `FunctionAgent`.

Yes. The framework lets you take the JSON output from any tool call and convert it into Document objects. You then embed those documents for future semantic search.

Start with `search_maritime_vessels` using the text name. Once you have the unique identifier, you pass it to the status or history tools for deeper tracking.

You restrict access using the `allowed_tools` parameter. If you only want the agent to see port data, just expose the port search and details tools.

Vinkius hosts the integration in a zero-trust, ephemeral environment. The specific port coordinates, country searches, and vessel names you query are processed in memory and immediately destroyed after execution.

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