How to Use the Zenserp MCP in Google ADK

Q: Can my agent check local businesses using Zenserp and Google ADK?

Yes. You use searchmaps to get current business listings and reviews. The results are structured, meaning your agents can act on the location data immediately.

Q: Does Zenserp support multiple search engines for Google ADK?

It does. You have access to searchbing, searchduckduckgo, and searchgoogle. This built-in redundancy is perfect for enterprise agents that can't afford a single point of failure.

Connect your enterprise agents to live search results from Zenserp, running natively within Google ADK on BigQuery.

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Connect Zenserp MCP to Google ADK

Create your Vinkius account to connect Zenserp 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 Zenserp with Google ADK

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Ingest Multi-Source Search Data

The `search_bing`, `search_duckduckgo`, and `search_google` tools pull organic search results from major engines. When using Google ADK, your agent can compare three different sources for the same query, guaranteeing data coverage. These functions let you feed real-time information directly into BigQuery or Vertex AI models used by your enterprise agents.

Manage Local and E-commerce Data

Your agent can retrieve product pricing via `search_shopping` or check local listings using `search_maps`. Plus, you get visual results with `search_images`, so your workflow isn't limited to just text. This lets you build complex enterprise processes that need both geo-location context and up-to-the-minute commerce data.

Handle Media and News Context

You can track breaking stories with `search_news`, or find relevant video content using `search_videos` and `search_youtube`. You also get specialized results from the `search_yandex` tool. This is key when building long-context reasoning agents in Google ADK that need to understand current market sentiment.

Setup guide

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

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Common questions about Zenserp MCP in Google ADK

You simply pass the MCP toolset into your LlmAgent. Since you're in the Gemini ecosystem, this lets your agents run search operations right alongside BigQuery queries.

Yes. You use `search_maps` to get current business listings and reviews. The results are structured, meaning your agents can act on the location data immediately.

It does. You have access to `search_bing`, `search_duckduckgo`, and `search_google`. This built-in redundancy is perfect for enterprise agents that can't afford a single point of failure.

It provides structured search results, news snippets, and media links. Because you run this in Google Cloud, all that data is ready to be processed by your existing BigQuery datasets.

The server touches live web search results (SERP), image metadata, and structured news articles. You'll be dealing primarily with textual and spatial data.

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