How to Use the Apify MCP in Google ADK

Q: Does Apify work with the Google ADK toolnames filter?

You can restrict the agent to specific tools like runactor or getdatasetresults. This limits the agent's scope to only what you need.

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Connect Apify MCP to Google ADK

Create your Vinkius account to connect Apify 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 Apify with Google ADK

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List and manage actors in Google ADK

Your agent uses `list_actors` to find the right scraper for the job. It maps your internal requirements to the correct task IDs. This avoids hardcoding IDs in your agent configuration. It makes your Google Cloud setup dynamic.

Process dataset collections via Google ADK

The agent calls `list_datasets` to see what information is available in your account. It then fetches specific content with `get_dataset_results`. This workflow integrates perfectly with your existing BigQuery data pipelines. Your agent acts as the bridge between the web and your warehouse.

Execute remote tasks from Google ADK

Trigger `run_actor` to start new extractions based on user prompts. It works as an autonomous tool call within your Google ADK framework. Your agent manages the entire task lifecycle. It keeps your infrastructure operations centralized in one place.

Setup guide

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

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Common questions about Apify MCP in Google ADK

Install the server via the McpToolset class in your Python code. Provide the Vinkius endpoint to the StreamableHttpServerParameters for a stable connection.

The server outputs clean JSON that the agent reads into its context. Since Gemini supports large token counts, you can pass entire datasets directly.

You can restrict the agent to specific tools like `run_actor` or `get_dataset_results`. This limits the agent's scope to only what you need.

The server logs every request to your Vinkius dashboard. You can cross-reference these with your agent execution logs in Google Cloud.

Your scraping tasks and dataset contents stay within your authenticated scope. The server uses token-based auth to prevent unauthorized access to your records.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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