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 AutoGen 10 tools — connect in under 2 minutes

Name: Octoparse
Availability: InStock
Author: Vinkius

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Octoparse as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

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

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="octoparse_agent",
            tools=tools,
            system_message=(
                "You help users with Octoparse. "
                "10 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

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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.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Octoparse tools. Connect 10 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

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 AutoGen 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 AutoGen via MCP

Follow these steps to integrate the Octoparse MCP Server with AutoGen.

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 10 tools from Octoparse automatically

Why Use AutoGen with the Octoparse MCP Server

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

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Octoparse tools to solve complex tasks

Role-based architecture lets you assign Octoparse tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive Octoparse tool calls

Code execution sandbox: AutoGen agents can write and run code that processes Octoparse tool responses in an isolated environment

Octoparse + AutoGen Use Cases

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

Collaborative analysis: one agent queries Octoparse while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from Octoparse, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using Octoparse data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process Octoparse responses in a sandboxed execution environment

Octoparse MCP Tools for AutoGen (10)

These 10 tools become available when you connect Octoparse to AutoGen 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 AutoGen

Ready-to-use prompts you can give your AutoGen 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 AutoGen

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

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

Octoparse + AutoGen FAQ

Common questions about integrating Octoparse MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Octoparse tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.