How to Use the Lusha MCP in Google ADK

Q: Can I restrict which Lusha tools my Google ADK agent uses?

Yes, use the toolnames filter during initialization. This limits the agent's access to only the endpoints you want it to touch.

Q: How do I monitor Lusha credit usage in Google ADK?

Call getcredits periodically within your agent loop. You can log these results to BigQuery to track your API spend over time.

Connect Lusha to your Google ADK environment to power Gemini agents with verified B2B data.

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Connect Lusha MCP to Google ADK

Create your Vinkius account to connect Lusha 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 Lusha with Google ADK

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Direct B2B data access

Link your Google ADK agent to Lusha to pull verified contact details instantly. Use `find_person` or `find_company` to fill out your outbound research tasks. Your agent queries the data and feeds it into your existing Google Cloud infrastructure. It works natively with your existing Vertex AI pipelines.

Efficient contact searching

Deploy `search_contacts` to let your agent build targeted lists based on your specific criteria. It handles the filtering logic within the Google ADK environment. This automates the prospecting phase of your sales cycle. You spend less time searching and more time closing deals with accurate data.

Infrastructure-ready integration

Utilize the McpToolset to expose Lusha capabilities to your Gemini models. It connects via HTTP or Stdio, fitting right into your Google Cloud architecture. You control which tools the agent can use. It keeps your setup lean and focused on the specific data points your team needs.

Setup guide

Set up Lusha 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 Lusha 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="Lusha_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Lusha tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Lusha MCP in Google ADK

It is. You initialize the McpToolset with your server URL and pass it to your LlmAgent. The agent then understands how to interact with the Lusha API.

The framework treats them as native tools. Your agent parses the requirements and calls `find_person` or other tools as needed for your specific task.

Yes, use the tool_names filter during initialization. This limits the agent's access to only the endpoints you want it to touch.

Your data remains in your controlled environment. The Lusha MCP server acts as a secure bridge, and you decide what information the agent processes.

Call `get_credits` periodically within your agent loop. You can log these results to BigQuery to track your API spend over time.

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