MCP For Claude Marketplace
Octoparse

Octoparse MCP Connector for Claude

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Connect your AI agent to Octoparse to trigger cloud web scraping tasks, monitor crawler statuses, and retrieve scraped data directly into chat.

10 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

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

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.

How it works

  1. Subscribe to this server
  2. Enter your Premium Octoparse API Credentials (Username/Email and Password)
  3. Command your agent (e.g., Claude or Cursor) to spin up scrapers and read the downloaded data directly onto your IDE

Who is this for?

  • Data Engineers — trigger scheduled pipelines, check extraction states, and dump JSON samples to debug schemas without leaving your terminal.
  • Growth Hackers — quickly spin up an Amazon or LinkedIn scraper, grab the extracted table data, and have the AI formulate email lists simultaneously.
  • Business Analysts — fetch the competitive pricing matrices scraped overnight and ask the AI to summarize price drops directly in the conversation.
data-extractionweb-crawlingno-codeautomationdata-pipelinecloud-scraping

10 tools expose this connector's capabilities to your AI agent.

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

See how to talk to your AI agent using Octoparse.

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

The Cloud Agent confirms the task 'LinkedIn Profiles Q4' finished running successfully and acquired `4523` rows of active data.

Start my Amazon Price Monitor crawler task now.

Task started. Your 'Amazon Price Monitor' has been queued to the cloud servers and will begin fetching targeted DOM elements shortly.

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

I've fetched the rows successfully. Here is the structured breakdown highlighting the `Address`, `Square Footage`, `Beds`, and estimated `Asking Price`...

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.

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