How to Use the ValueSERP MCP in Google ADK

Q: How do I integrate ValueSERP with Google ADK for scholarly research?

Your agent calls the googlescholarsearch tool. This pulls academic publications and abstracts into your workflow, which you can then process using Gemini's large context window.

Q: What is the best way to get diverse search inputs for Google ADK?

Use the customserpsearch tool. It allows full control over parameters, giving you structured data that your BigQuery-native agent can consume and reason upon.

Q: Can ValueSERP help my Google ADK monitor product trends?

Yes. The googleshoppingsearch tool retrieves real-time product names, prices, and merchant links. This data stream is useful for market analysis within your enterprise agents.

Q: Does the MCP Server support finding local business data with Google ADK?

It does. The googleplacessearch tool specifically requires a place name and location, giving you reliable geo-data for your models to reference.

Feeding Enterprise Search Data into Your Google ADK Pipelines via MCP Server.

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Connect ValueSERP MCP to Google ADK

Create your Vinkius account to connect ValueSERP 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 ValueSERP with Google ADK

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Structured Research for BigQuery Integration

The `custom_serp_search` tool allows your agent to execute highly customized Google searches, delivering results as a structured JSON object. This format is ideal for piping directly into BigQuery tables. The tool also gives you specialized access points like `google_scholar_search`, providing abstracts and academic publication details that can be ingested alongside internal data.

Comprehensive Data Gathering with MCP Server

You don't have to run multiple queries. Use the ValueSERP MCP Server for related context via `get_related_questions` or predictive inputs using `get_search_suggestions`. This keeps your long-context Gemini model fed with rich, varied data. When tracking commercial activity, running `google_shopping_search` provides product names and prices that can be analyzed against internal inventory records.

Geo-Contextualizing Data Streams

Need to ground your agents in reality? The `google_places_search` tool finds local businesses by requiring a place name and specific location. This grounds the search data geographically. Additionally, if you're building pipelines around current events, use `google_news_search`. It delivers structured news articles that augment your enterprise datasets.

Setup guide

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

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Common questions about ValueSERP MCP in Google ADK

Your agent calls the `google_scholar_search` tool. This pulls academic publications and abstracts into your workflow, which you can then process using Gemini's large context window.

Use the `custom_serp_search` tool. It allows full control over parameters, giving you structured data that your BigQuery-native agent can consume and reason upon.

Yes. The `google_shopping_search` tool retrieves real-time product names, prices, and merchant links. This data stream is useful for market analysis within your enterprise agents.

It does. The `google_places_search` tool specifically requires a place name and location, giving you reliable geo-data for your models to reference.

The server touches multiple data types: organic search text results, image URLs, structured product price lists, news articles, and academic abstracts.

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