How to Use the Octoparse MCP in AutoGen

Q: How do AutoGen agents manage Octoparse tools?

The agents share a common pool of MCP tools. They call listtasks and gettaskdata as needed during their conversation to reach a conclusion.

Q: How do I secure Octoparse API access for AutoGen?

The MCP server handles the gettoken handshake. Your agents never see your raw credentials, only the temporary access token.

Q: Is it possible to automate the export process in AutoGen?

You can set up an agent to run markdataexported after every successful data retrieval. It ensures your task status is always accurate.

Debate and execute Octoparse scraping workflows with AutoGen multi-agent teams.

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Setup Octoparse with AutoGen

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Perplexity

Multi-agent Octoparse coordination

Let your agents debate whether to `start_task` or wait for a better time. AutoGen allows your security and data agents to agree on the right move. It creates a consensus-driven approach to web scraping. Agents verify status with `get_task_status` before they decide to retrieve data.

Automated data cleanup

Configure an agent to run `clear_task_data` once the team finishes extraction. This keeps your account clean without human oversight. It automates the housekeeping tasks that usually consume your time. Your agents manage the entire lifecycle from start to finish.

Parameter updates via negotiation

Use `update_task_params` to adjust your scraping strategy based on agent feedback. If one agent finds a better URL, the team updates the task configuration together. This makes your crawlers adaptive. You gain a system that learns and adjusts its own scraping parameters in real-time.

Setup guide

Set up Octoparse MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Octoparse tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Octoparse_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Octoparse data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Octoparse_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Octoparse data")
    print(result.messages[-1].content)

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Common questions about Octoparse MCP in AutoGen

The agents share a common pool of MCP tools. They call `list_tasks` and `get_task_data` as needed during their conversation to reach a conclusion.

Yes, agents can challenge each other's decisions. One might suggest stopping a task via `stop_task` if it consumes too many resources.

The MCP server handles the `get_token` handshake. Your agents never see your raw credentials, only the temporary access token.

The server shares only the task metadata and the scraped content. Your sensitive account settings remain restricted by the API's internal permissions.

You can set up an agent to run `mark_data_exported` after every successful data retrieval. It ensures your task status is always accurate.

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