How to Use the URL Metadata Scraper MCP in Google ADK

Q: How does the URL Metadata Scraper fit into Google ADK workflows?

You can integrate scrapeurl as a toolset for your LlmAgent. Your agents call this to get external data, which then feeds directly into Gemini's reasoning cycle alongside your BigQuery data.

Pull structured metadata into your Google ADK agents for enterprise data action.

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Connect URL Metadata Scraper MCP to Google ADK

Create your Vinkius account to connect URL Metadata Scraper 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 URL Metadata Scraper with Google ADK

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Instant SEO Data Retrieval

The `scrape_url` tool pulls OpenGraph and standard SEO data—titles, descriptions, and images—from any URL. This process happens instantly without needing to load a full headless browser session. Your agents can use this speed to enrich massive datasets in BigQuery before running complex analyses.

BigQuery Integration Source

Use the metadata gathered by `scrape_url` as fresh data to augment records already housed in BigQuery. The tool provides clean, structured JSON containing titles and descriptions. This lets your Gemini-powered agents execute deeper reasoning on real-time web context.

Enterprise Data Enrichment

The `scrape_url` function reliably extracts key metadata fields needed for marketing or content teams. It pulls OpenGraph tags like Title and Description into a format that supports long-context reasoning. This is critical when building agents that need to correlate internal data with external web context.

Setup guide

Set up URL Metadata Scraper 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 URL Metadata Scraper 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="URL Metadata Scraper_agent",
    model="gemini-2.0-flash",
    instruction="You have access to URL Metadata Scraper tools via MCP.",
    tools=mcp_tools,
)

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Common questions about URL Metadata Scraper MCP in Google ADK

You can integrate `scrape_url` as a toolset for your LlmAgent. Your agents call this to get external data, which then feeds directly into Gemini's reasoning cycle alongside your BigQuery data.

Yes, pass a list of URLs through the tool. The agent framework handles iterating over these calls, allowing you to gather metadata for an entire batch efficiently.

It provides Title, Description, and Image URLs. These are clean strings that your agent can consume, making them perfect candidates for structured analysis in Vertex AI.

No. Since it extracts metadata directly without loading a full browser, it's very light on compute and doesn't stress your Google Cloud infrastructure.

This server touches public web metadata, specifically OpenGraph tags and basic SEO information like titles and descriptions found at a given URL.

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