How to Use the Lamha MCP in CrewAI

Q: How do I configure Lamha MCP tools for a CrewAI agent?

Pass the Lamha URL directly into the agent's mcps parameter, or use the advanced MCPServerHTTP class to filter specific tools like createorder.

Deploy specialized multi-agent crews to manage Arabic logistics using CrewAI.

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Connect Lamha MCP to CrewAI

Create your Vinkius account to connect Lamha 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.

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Setup Lamha with CrewAI

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Run multi-agent operations with CrewAI

The `list_orders` tool gives your CrewAI monitoring agent a clear view of all active shipments in the Middle East. While one agent tracks order anomalies, another specialized agent can use `get_order` to investigate specific customer complaints using the active MCP Server. This division of labor mirrors a real-world operations team. By feeding this server directly to your Python crew, your agents collaborate autonomously to resolve logistics bottlenecks in Arabic.

Automate warehouse allocation across regions

The `list_warehouses` tool provides your inventory agent with the exact geographic locations of your regional hubs. When a customer orders an item, the agent matches the request against `list_inventory` using the MCP connection to find the closest fulfillment center. This keeps shipping times low and reduces domestic transit costs. The agents coordinate these checks entirely in the background, updating your database without manual intervention.

Proactive delivery coverage validation

The `check_city_coverage` tool is used by your pre-sales agent to verify delivery routes before confirming a purchase. If a customer inquires about a remote town in Saudi Arabia, the agent checks regional carrier networks immediately. This stops undeliverable orders before they are even packed. Your CrewAI agents handle the entire vetting process, only escalating to human staff when no carrier is found.

Setup guide

Set up Lamha 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 Lamha tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the Lamha URL directly into the agent's `mcps` parameter, or use the advanced `MCPServerHTTP` class to filter specific tools like `create_order`.

Yes, CrewAI's shared memory system allows different agents to access tools like `list_inventory` and `list_carriers` during the same execution loop.

Yes, you can connect your crew to Lamha using stdio, SSE, or Streamable HTTP MCP transports depending on your hosting setup.

The server has built-in natural language understanding for Middle Eastern dialects, translating casual Arabic queries into structured parameters for the tools.

Yes, all e-commerce order details, city coverage queries, and customer shipping data are processed in zero-trust, ephemeral V8 isolates running this MCP Server. No data is cached or stored permanently on the server.

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