How to Use the Zenserp MCP in AutoGen

Q: How does AutoGen use Zenserp for deliberation?

The server provides multiple search engine APIs (searchgoogle, searchbing) that give opposing views on a query. The agents debate these different results until they reach an agreed-upon answer, simulating real-world consensus.

Q: Can Zenserp handle different regions for AutoGen's debate?

The searchgoogle tool accepts location strings. You can build a scenario where one agent researches 'London, UK' and another researches 'Tokyo, JP,' forcing them to debate the international differences.

Drive consensus decisions with AutoGen and the Zenserp MCP Server.

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Create your Vinkius account to connect Zenserp 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 Zenserp with AutoGen

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Facilitate debate on search findings

You can set up an agent system where one agent uses `search_bing` to gather initial data, while a second agent challenges those results using the `search_duckduckgo` tool. They negotiate which source is more reliable. The MCP Server provides competing views of the same query, forcing your multi-agent system to deliberate and converge on a single, robust conclusion.

Validate market data from multiple sources

Build a 'Validation Agent' that runs `search_shopping` and then passes those results to a second agent using the `search_yandex` tool. The agents argue whether the price difference is due to region or seller, resulting in a consensus report. This process moves beyond simple execution; it forces critical evaluation of contradictory API data.

Develop complex research workflows

An agent can be configured to first check current news via `search_news`, then pull related images using `search_images`. The third agent reviews the combination, flagging inconsistencies between media and text. The framework handles the handoff of multi-format data—text, image URLs, video results—allowing agents to build complex, reasoned arguments.

Setup guide

Set up Zenserp 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 Zenserp 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="Zenserp_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zenserp 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="Zenserp_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zenserp 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 Zenserp. 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 Zenserp MCP in AutoGen

The server provides multiple search engine APIs (`search_google`, `search_bing`) that give opposing views on a query. The agents debate these different results until they reach an agreed-upon answer, simulating real-world consensus.

Yes. You can assign specific roles—like 'Verifier' or 'Market Analyst'—to agents and task them with comparing results from tools like `search_shopping` against local listings from `search_maps`.

The system handles various formats: text snippets, structured product pricing data, and media URLs. The agent framework manages the schema conversion to pass these diverse inputs back and forth.

It is high-speed and verifiable because it scrapes live search engine results (SERP) from multiple sources. This keeps the agents arguing over fresh, real-time data.

The `search_google` tool accepts location strings. You can build a scenario where one agent researches 'London, UK' and another researches 'Tokyo, JP,' forcing them to debate the international differences.

Use it with your favorite AI tools

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