How to Use the VesselAPI MCP in CrewAI

Q: How do I run VesselAPI searches with CrewAI?

You assign the searchvessels tool to your research agent. The team executes this step, providing a list of candidates that subsequent agents can then process for deeper analysis.

Q: How do I get vessel details using VesselAPI with CrewAI?

You give the 'Analysis Agent' access to getvesseldetails. Once it receives an IMO number from another agent, it autonomously calls this tool and integrates the findings into its report.

Q: What if I need to list ports using VesselAPI with CrewAI?

You simply assign listmaritimeports as a foundational task for one of your agents. This provides the necessary context (the full list of supported locations) for other parts of the operational workflow.

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Setup VesselAPI with CrewAI

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Automate vessel discovery and analysis.

Design a 'Research Agent' that first calls `search_vessels` based on user input. Then, a second 'Analysis Agent' takes the resulting names and uses `get_vessel_details` to pull all necessary operational data. This role-based specialization means agents collaborate: one finds the vessel, another analyzes its status using the MCP Server.

Monitor real-time positions continuously.

Set up a 'Monitoring Agent' tasked with calling `get_vessel_position` on a recurring basis. A separate 'Escalation Agent' watches this stream of data and triggers an alert if the position is static for too long. CrewAI handles this continuous, multi-step observation process without manual intervention.

Audit port schedules autonomously.

Create a 'Logistics Agent' that first calls `list_maritime_ports` to get the available global ports. It then uses this list and the target vessel's name to call `get_vessel_schedules`, building a complete schedule report. This autonomous operation works like a team effort, combining multiple tool results into one final output.

Setup guide

Set up VesselAPI MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke VesselAPI tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="VesselAPI Analyst",
    goal="Access and analyze VesselAPI data via MCP.",
    backstory="Expert analyst with direct VesselAPI access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent VesselAPI transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="VesselAPI Analyst",
        goal="Access and analyze VesselAPI data via MCP.",
        backstory="Expert analyst with direct VesselAPI access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent VesselAPI transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by VesselAPI. 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 VesselAPI MCP in CrewAI

You assign the `search_vessels` tool to your research agent. The team executes this step, providing a list of candidates that subsequent agents can then process for deeper analysis.

Yes. You pass the URL in the Agent mcps array. This allows your entire crew to interact with this specific MCP Server, treating all its tools as a shared resource.

You give the 'Analysis Agent' access to `get_vessel_details`. Once it receives an IMO number from another agent, it autonomously calls this tool and integrates the findings into its report.

You simply assign `list_maritime_ports` as a foundational task for one of your agents. This provides the necessary context (the full list of supported locations) for other parts of the operational workflow.

This MCP Server manages AIS positions, IMO numbers, and port schedules. Since CrewAI runs autonomous operations, ensure your agent roles are strictly scoped to prevent unauthorized access or logging of sensitive vessel movements.

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