How to Use the Datalastic Maritime MCP in AutoGen

Q: How do I configure Datalastic Maritime with AutoGen?

Install autogen-extmcp. Use mcpservertools with a StreamableHttpServerParams pointing to your Vinkius URL, then pass the resulting list to your AssistantAgent constructor.

Q: Can multiple AutoGen agents use Datalastic Maritime simultaneously?

Yes. The McpToolAdapter handles the schema conversion, allowing any agent in the conversation group to trigger a vessel search or port query when it has the floor.

Deploy debating AutoGen agents to analyze maritime logistics. Let competing AI models negotiate shipping routes.

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Connect Datalastic Maritime MCP to AutoGen

Create your Vinkius account to connect Datalastic Maritime to AutoGen 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 AutoGen

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Perplexity

Multi-agent vessel tracking

AutoGen shines when there is no single right answer. You assign one agent to act as a port authority and another as a fleet manager. The fleet manager calls `get_vessel_status` to check a ship's position, while the port authority uses `get_port_details` to verify terminal capacity. They argue over the best approach. If the fleet manager wants to divert a ship, the port authority pushes back based on the draft restrictions it pulled from the MCP server. They keep debating until they reach a logistical consensus.

Analyze AutoGen MCP Server risks

Congestion creates supply chain risk. A security agent triggers `find_vessels_in_radius` around a specific coordinate to check for overcrowding. If it sees too many tankers, it flags the area as a high-risk zone. A logistics agent then reviews that data. It calls `get_vessel_history` on the delayed ships to calculate exactly how much time they are losing. The two agents synthesize this data to recommend a completely different port via `search_ports_by_country`.

Debate technical ship capabilities

Sometimes you need to match cargo requirements against physical reality. One agent queries `search_maritime_vessels` to build a shortlist of available bulk carriers. It hands those IDs over to a technical inspection agent. The inspector agent runs `get_vessel_pro_specs` on every candidate. It scrutinizes the deadweight tonnage and dimensions, rejecting any ship that fails the physical requirements for the planned route. The conversation ends when a suitable vessel is agreed upon.

Setup guide

Set up Datalastic Maritime MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Datalastic Maritime tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Datalastic Maritime_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Datalastic Maritime data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Datalastic Maritime_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Datalastic Maritime data")
    print(result.messages[-1].content)

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Common questions about Datalastic Maritime MCP in AutoGen

Install `autogen-ext[mcp]`. Use `mcp_server_tools` with a `StreamableHttpServerParams` pointing to your Vinkius URL, then pass the resulting list to your `AssistantAgent` constructor.

Yes. The `McpToolAdapter` handles the schema conversion, allowing any agent in the conversation group to trigger a vessel search or port query when it has the floor.

The tool returns an empty result or error string to the chat. The agent reads that failure, realizes its mistake, and usually tries a different spelling with the search tool.

Complex logistics require competing perspectives. By splitting the tracking, spec validation, and port selection into separate personas, you get a system that double-checks its own work before making a routing decision.

The Vinkius architecture removes all persistent state. The AIS track history, vessel IDs, and port coordinates your agents debate never touch a database, ensuring your shipping logistics remain entirely confidential.

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