How to Use the DCL Logistics MCP in CrewAI
Assemble a CrewAI team to autonomously audit DCL Logistics inventory, monitor order fulfillment, and resolve shipping delays.
Works with every AI agent you already use
…and any MCP-compatible client
Connect DCL Logistics MCP to CrewAI
Create your Vinkius account to connect DCL Logistics to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Connect your MCP Server to specialized agents
`list_recent_shipments` feeds tracking numbers and departure timestamps straight to your monitoring agent. You assign one CrewAI worker specifically to watch outbound freight traffic. Another agent handles stock levels using `list_warehouse_inventory` to pull total on-hand and reserved quantities. These workers share memory, meaning the shipping agent knows exactly what the inventory agent just found.
Resolve stalled fulfillment autonomously
`search_orders_by_keyword` locates specific fulfillment records using customer names or shipping addresses. Your research agent finds the exact order that triggered a customer complaint. The moderator agent then steps in with `get_order_details` to read the audit trail of processing events. It decides if the delay warrants a refund or just an email update, executing the decision hierarchically.
Process RMAs without human input
`list_customer_returns` lists every pending RMA, including the status of returned goods and credit processing info. You configure a sequential process where Agent A reads the return reason and Agent B approves the credit. Before finalizing anything, the manager agent verifies the active service tier via `get_account_details`. Your MCP Server lets the crew run the entire reverse logistics operation in the background.
Set up DCL Logistics MCP in CrewAI
Prerequisites
- Python 3.10+ installed
-
crewaipackage (pip install crewai) - Active Vinkius subscription with a valid endpoint token
- 1
Install CrewAI
Run
pip install crewaito install the framework. MCP support is built-in via themcpsparameter. - 2
Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the
mcpslist. 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
Crewwith your agent and tasks. Callcrew.kickoff()— the agent will automatically invoke DCL Logistics tools as needed.
from crewai import Agent, Task, Crew
agent = Agent(
role="DCL Logistics Analyst",
goal="Access and analyze DCL Logistics data via MCP.",
backstory="Expert analyst with direct DCL Logistics access.",
mcps=[
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
],
)
task = Task(
description="List recent DCL Logistics transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Prerequisites
- Python 3.10+ installed
-
crewai+crewai-toolspackages - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
pip install crewai crewai-tools. TheMCPServerAdapterhandles lifecycle management and tool conversion. - 2
Connect with MCPServerAdapter
Use
MCPServerAdapteras a context manager withSseServerParameterspointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle. - 3
Assign tools and run
Pass the returned
mcp_toolsto your agent'stoolsparameter. The adapter converts MCP tools to nativeBaseToolobjects compatible with all CrewAI agents.
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="DCL Logistics Analyst",
goal="Access and analyze DCL Logistics data via MCP.",
backstory="Expert analyst with direct DCL Logistics access.",
tools=mcp_tools,
)
task = Task(
description="List recent DCL Logistics transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DCL Logistics. 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 DCL Logistics MCP in CrewAI
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