How to Use the Lamha MCP in Google ADK

Q: How do I configure Lamha with Google ADK?

Wrap your endpoint in StreamableHttpServerParameters and pass it to McpToolset. Assign that toolset to your LlmAgent initialization array.

Q: Can Google ADK restrict which Lamha tools the agent sees?

You pass an array of strings to the toolnames filter. This lets you expose listwarehouses while hiding mutation endpoints from public-facing Gemini models.

Connect Gemini agents to Lamha's MCP logistics using Google ADK for enterprise Arabic e-commerce.

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Connect Lamha MCP to Google ADK

Create your Vinkius account to connect Lamha to Google ADK 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 Google ADK

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Process bulk orders with Google ADK

The Lamha integration gives your Gemini models access to `list_orders` and `list_inventory` right alongside your BigQuery datasets. You feed thousands of historical Arabic customer chats into the context window. Gemini extracts the specific logistics intents from those massive transcripts. It then executes targeted queries against the Lamha endpoint to audit past fulfillment performance.

Automate Middle East MCP logistics

Your agent uses `check_city_coverage` and `list_carriers` to route shipments based on regional dialect inputs. The Google ADK framework passes the user's Arabic prompt directly to the toolset. You filter exposed tools using the `tool_names` parameter. A customer service bot only gets access to read operations, preventing accidental modifications to live logistics pipelines.

Execute transactions on Vertex AI

Enterprise workflows require reliable execution for `create_order` and `cancel_order` operations. The ADK links these Lamha tools to your existing Google Cloud infrastructure. A user types a cancellation request in Gulf Arabic. The Gemini model parses the intent, pulls the ID via `get_order`, and processes the cancellation instantly.

Setup guide

Set up Lamha MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Lamha tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Lamha_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Lamha tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Lamha MCP in Google ADK

Wrap your endpoint in `StreamableHttpServerParameters` and pass it to `McpToolset`. Assign that toolset to your `LlmAgent` initialization array.

You pass an array of strings to the `tool_names` filter. This lets you expose `list_warehouses` while hiding mutation endpoints from public-facing Gemini models.

The Lamha server features built-in natural language understanding for Middle Eastern dialects. Gemini passes the raw Arabic text, and the server maps it to structured logistics arguments.

The ADK supports both Stdio and HTTP transports natively. You just point the Streamable HTTP client at your managed Vinkius URL.

The integration only reads the exact parameters required for tools like `check_city_coverage`. Inventory counts and carrier lists route through an encrypted, ephemeral sandbox. Vinkius drops all state the moment your Vertex AI instance closes the socket.

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