How to Use the ZenRows MCP in AutoGen

Q: How does AutoGen use ZenRows for screenshot proof?

Agents call getscreenshot to gather visual context. The output is a URL that confirms the state of the page, which one agent can then present as evidence in the debate cycle.

Q: Can AutoGen use ZenRows for complex scraping tasks?

Yes. You can combine scrapejs with scrapeantibot to ensure that no matter how difficult or dynamic a page is, your agents get the full context needed to argue their point.

Q: Does ZenRows support AutoGen for multi-market comparisons?

The scrapegeo tool lets you scrape content from specific countries. This allows two competing agents—say, a market analyst and a legal agent—to gather localized data to debate which region presents the highest risk.

Facilitate consensus decisions with AutoGen using ZenRows MCP Server.

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Connect ZenRows MCP to AutoGen

Create your Vinkius account to connect ZenRows to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ZenRows with AutoGen

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Gather visual evidence for debate in AutoGen.

When agents argue, they sometimes need to see proof. Use `get_screenshot` to capture a URL that returns an image of the target page. This provides undeniable, visual evidence for the deliberation process. One agent can present this screenshot as a piece of data that all other agents must reconcile and debate.

Manage timing dependencies in AutoGen.

If one agent needs to scrape content only after another has performed an action, `scrape_wait` is key. It pauses the conversation flow until a specific CSS selector appears on the page. This prevents agents from operating on outdated or incomplete data during their debate.

Guarantee access to critical information via AutoGen.

Sometimes, the best information is behind paywalls or anti-bot measures. Agents can use `scrape_premium` to ensure high anonymity and bypass these blocks. This gives them equal footing in the debate. This ensures that consensus isn't accidentally built on incomplete or restricted data.

Setup guide

Set up ZenRows 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 ZenRows 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="ZenRows_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="ZenRows_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ZenRows. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ZenRows MCP in AutoGen

Agents call `get_screenshot` to gather visual context. The output is a URL that confirms the state of the page, which one agent can then present as evidence in the debate cycle.

Yes. You can combine `scrape_js` with `scrape_antibot` to ensure that no matter how difficult or dynamic a page is, your agents get the full context needed to argue their point.

The `scrape_geo` tool lets you scrape content from specific countries. This allows two competing agents—say, a market analyst and a legal agent—to gather localized data to debate which region presents the highest risk.

The server touches raw HTML, structured textual content, and visual references (screenshots). This variety of input forces your agents to deliberate across multiple data types.

It is. The tools are designed to be robust against anti-bot measures, ensuring that the information gathered remains stable and usable throughout the multi-agent debate cycle.

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