How to Use the Cedar AI MCP in CrewAI

Q: How do I connect Cedar AI to my CrewAI setup?

Add the Vinkius HTTP URL directly to the mcps array in your Agent definition. For advanced setups, import MCPServerHTTP from crewai.mcp and configure specific tool filters.

Q: How do I restrict which agents can update Cedar AI work orders?

Use the toolfilter parameter when defining your MCP Server connection. You give read-only tools like getworkorderdetails to your monitor agents, and reserve updateworkorder for your action agents.

Q: Does this setup require Python?

Yes. You install the framework using pip install crewaitools. Your agents and tasks are defined in Python scripts that execute locally or on your servers.

Deploy a CrewAI autonomous team to manage your rail yard, processing work orders and updating train schedules without human intervention.

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Setup Cedar AI with CrewAI

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Perplexity

Autonomous Yard Management with CrewAI

Running a rail terminal requires multiple roles working together. You spin up a crew of specialized agents instead of relying on a single bot. One agent acts as the inventory clerk while another handles dispatch. The clerk constantly polls `list_inventory` and `list_work_orders`. When a new train rolls in, the clerk passes the manifest to the dispatcher agent, who executes `arrive_train` and assigns tracks.

Hierarchical Train Operations

Complex yard movements need a chain of command. You set up a manager agent that oversees the entire operation. It delegates specific tasks to subordinate agents based on their assigned tools. An inspector agent uses `get_railcar_details` to check for bad orders. If it finds a damaged car, it reports back to the manager. The manager then orders a repair agent to run `update_railcar_status` to mark it out of service.

Complete Waybill Auditing

Finding missing freight usually means digging through databases for hours. Your auditing crew does this automatically. An agent pulls all current records using `list_waybills`. A secondary monitor agent cross-references those waybills against actual cars sitting in the yard via `get_waybill_details`. The MCP Server feeds accurate data directly into the crew's shared memory.

Setup guide

Set up Cedar AI 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 Cedar AI tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Cedar AI 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="Cedar AI Analyst",
        goal="Access and analyze Cedar AI data via MCP.",
        backstory="Expert analyst with direct Cedar AI access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Cedar AI 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 Cedar AI. 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 Cedar AI MCP in CrewAI

Add the Vinkius HTTP URL directly to the `mcps` array in your Agent definition. For advanced setups, import `MCPServerHTTP` from `crewai.mcp` and configure specific tool filters.

Yes. CrewAI maintains shared memory across the team. If Agent A calls `get_railcar_details`, Agent B can use that exact data to execute `pickup_cars` later in the sequence.

Use the `tool_filter` parameter when defining your MCP Server connection. You give read-only tools like `get_work_order_details` to your monitor agents, and reserve `update_work_order` for your action agents.

Yes. You install the framework using `pip install crewai[tools]`. Your agents and tasks are defined in Python scripts that execute locally or on your servers.

Everything routes through a V8 Isolate Sandbox on Vinkius. Your arrival times, departure logs, and railcar IDs are processed in an ephemeral environment that destroys itself the second the connection closes.

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