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 LangChain 10 tools — connect in under 2 minutes

Name: Octoparse
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Octoparse through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "octoparse": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Octoparse, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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.

LangChain's ecosystem of 500+ components combines seamlessly with Octoparse through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

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

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Octoparse via MCP

Why Use LangChain with the Octoparse MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Octoparse MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Octoparse queries for multi-turn workflows

Octoparse + LangChain Use Cases

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

RAG with live data: combine Octoparse tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Octoparse, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Octoparse tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Octoparse tool call, measure latency, and optimize your agent's performance

Octoparse MCP Tools for LangChain (10)

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

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

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Octoparse + LangChain FAQ

Common questions about integrating Octoparse MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.