How to Use the HTML to Text Extractor MCP in Google ADK

Q: How do I add the HTML to Text Extractor to Google ADK?

Create an McpToolset instance using StreamableHttpServerParameters and your endpoint URL. Add this MCP Server to the tools array when building your LlmAgent.

Feed clean text to your Google ADK enterprise agents and save massive token costs.

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Connect HTML to Text Extractor MCP to Google ADK

Create your Vinkius account to connect HTML to Text Extractor 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 HTML to Text Extractor with Google ADK

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Scale BigQuery Ingestion via MCP Server

`extract_text` processes raw HTML fields pulled directly from your BigQuery tables. Enterprise datasets often contain legacy web content, and you need to strip that markup before passing it to Vertex AI. Configure `McpToolset` with your Vinkius URL and attach it to your LlmAgent. Even though Gemini handles 1M+ tokens, filling that context window with CSS classes slows down reasoning. Clean text keeps your agent focused and fast.

Clean Web Scrapes for Gemini

`extract_text` removes all the structural noise from web pages so your Google ADK agent can actually read the content. Raw HTML is a terrible format for semantic analysis. The tool drops scripts, styles, and tags, returning only the human-readable words. You can use the `tool_names` filter in your setup to ensure the agent only exposes this specific MCP Server capability when processing web data.

Process Legacy Intranet Pages

`extract_text` handles the brutal, nested table layouts of older enterprise portals. When your agent navigates internal company wikis, it usually hits a wall of terrible formatting. By parsing the DOM and extracting just the text, your agent gets the actual knowledge base articles without the visual clutter. The Google ADK handles the long-context reasoning over the resulting text, letting you build massive internal search tools.

Setup guide

Set up HTML to Text Extractor 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 HTML to Text Extractor 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="HTML to Text Extractor_agent",
    model="gemini-2.0-flash",
    instruction="You have access to HTML to Text Extractor tools via MCP.",
    tools=mcp_tools,
)

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Common questions about HTML to Text Extractor MCP in Google ADK

Create an `McpToolset` instance using `StreamableHttpServerParameters` and your endpoint URL. Add this MCP Server to the `tools` array when building your LlmAgent.

Absolutely. The agent natively routes the extracted text into Gemini's context window. You save tokens and speed up the time to first token.

The tool processes one HTML string per call. Your agent can loop through BigQuery records and call the tool sequentially for bulk processing.

Because attention degrades over long contexts, and processing garbage markup costs money. Stripping HTML improves the signal-to-noise ratio for the model.

The server runs entirely in memory and never logs your intranet HTML or extracted text. Once the tool returns the plain string to Google ADK, the processing container is destroyed.

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