Can I have my AI format the scraped JSON into a clean Markdown table?

Absolutely. Because Octoparse MCP connects natively via the `get_task_data` capability directly into the AI's isolated context window, the language model can instantly translate cumbersome JSON fields into polished, structured, and legible tabular outputs on demand.

Is it possible to track task progress percentage in the chat?

Yes. When you instruct your agent to run `get_task_status`, it fetches the real-time runtime progress metrics from Octoparse's cloud. You'll see whether it's Waiting, Running, or Completed, along with how many rows have been extracted so far.

Do I need a paid Octoparse plan for API capabilities to work?

Yes. Octoparse explicitly limits their Advanced Cloud APIs strictly to their paid subscription levels. A Free tier account will reject the authentication tokens when attempting to fetch the runtime data.

Octoparse MCP Server for Google ADK 10 tools — connect in under 2 minutes

Name: Octoparse
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Octoparse as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="octoparse_agent",
    instruction=(
        "You help users interact with Octoparse "
        "using 10 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Octoparse MCP Server

Connect your Octoparse framework to your AI agent and turn cloud-based web scraping into a fully conversational command center.

Google ADK natively supports Octoparse as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 10 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Task Execution — Trigger the launch engine using start_task whenever data refresh is needed, or invoke stop_task to halt runaway crawlers instantly.
Status Monitoring — Keep a pulse on active bots by calling get_task_status, or systematically drill down through your project taxonomy via list_task_groups and list_tasks.
Data Ingestion — Dump the latest extracted web rows natively into the AI's context using get_task_data, allowing the LLM to format, structure, or summarize the results immediately.
Token Operations — Authenticate dynamically utilizing get_token with your core credentials.

The Octoparse MCP Server exposes 10 tools through the Vinkius. Connect it to Google ADK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Octoparse to Google ADK via MCP

Follow these steps to integrate the Octoparse MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 10 tools from Octoparse via MCP

Why Use Google ADK with the Octoparse MCP Server

Google ADK provides unique advantages when paired with Octoparse through the Model Context Protocol.

Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Octoparse

Production-ready features like session management, evaluation, and deployment come built-in. not bolted on

Seamless integration with Google Cloud services means you can combine Octoparse tools with BigQuery, Vertex AI, and Cloud Functions

Octoparse + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Octoparse MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Octoparse and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Octoparse tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Octoparse regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Octoparse

Octoparse MCP Tools for Google ADK (10)

These 10 tools become available when you connect Octoparse to Google ADK via MCP:

clear_task_data

Done to purge testing footprints before production crawls. Delete all securely stored data for an Octoparse task

get_task_data

Use offset-based pagination strictly to prevent memory crash exceptions (max 1000 limit). Export un-exported data from a completed Octoparse scraping task

get_task_status

Get the current running status of an Octoparse cloud task

get_token

0 password grant. Returns an access_token. The access_token must be stored and reused for API calls until expiration. Obtain an OAuth 2.0 access token from Octoparse

list_task_groups

Use these IDs to filter executing scraping tasks nested inside a specific folder limit. List all task groups (folders) in the Octoparse account

list_tasks

Each task includes a taskId, status, and creation date. Use the taskId for starting or polling data. List all configured cloud scraping tasks on Octoparse

mark_data_exported

Execute this immediately after a successful `get_task_data`. Mark all currently stored data in an Octoparse task as extracted

start_task

Task changes status to Running instantly. Start a cloud scraping task on Octoparse

stop_task

Stop a running Octoparse cloud task

update_task_params

g. changing the core search URL or injected keywords) without opening the Octoparse IDE cleanly scaling parameterized bots. Dynamically update URL or parameter constraints driving a task

Example Prompts for Octoparse in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Octoparse immediately.

"Look up task 'LinkedIn Profiles Q4' and tell me how many rows it extracted."

"Start my Amazon Price Monitor crawler task now."

"Get the data extracted from task 'Real Estate NYC' and format it as a markdown table."

Troubleshooting Octoparse MCP Server with Google ADK

Common issues when connecting Octoparse to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Octoparse + Google ADK FAQ

Common questions about integrating Octoparse MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.