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 CrewAI 10 tools — connect in under 2 minutes

Name: Octoparse
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Octoparse through Vinkius, pass the Edge URL in the `mcps` parameter and every Octoparse tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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 crewai import Agent, Task, Crew

agent = Agent(
    role="Octoparse Specialist",
    goal="Help users interact with Octoparse effectively",
    backstory=(
        "You are an expert at leveraging Octoparse tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Octoparse "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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60%Token savings

High SecurityEnterprise-grade

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DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

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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.

When paired with CrewAI, Octoparse becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Octoparse tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Octoparse

Why Use CrewAI with the Octoparse MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Octoparse through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Octoparse + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Octoparse for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Octoparse, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Octoparse tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Octoparse against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Octoparse MCP Tools for CrewAI (10)

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

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

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Octoparse + CrewAI FAQ

Common questions about integrating Octoparse MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.