How to Use the ValueSERP MCP in AutoGen

Q: Can I have multiple agents analyze different media types using ValueSERP?

Yes. One agent can gather general articles via googlenewssearch, while another uses googleimagesearch to provide visual context. The conversation then converges on how these two data streams relate.

Debate Complex Decisions with AutoGen. ValueSERP feeds real Google search data into your multi-agent conversation framework.

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Connect ValueSERP MCP to AutoGen

Create your Vinkius account to connect ValueSERP 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 ValueSERP with AutoGen

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Perplexity

Consensus on Market Viability

Need an expert opinion on a new product? Have one agent run `google_shopping_search` to gather current pricing and competitor data. A second agent can then critique this data, challenging the assumptions about market demand. The third agent might use `get_related_questions` to identify gaps in the existing knowledge base. The agents debate until a consensus on viability is reached.

Deliberation Across Media Types

If you're planning a launch, your multi-agent system can discuss it thoroughly. One agent runs `google_search` for general sentiment. Another uses `google_video_search` to check competitor marketing tactics. They debate whether video content or written articles should be the priority. The MCP Server provides the raw, diverse data—from text to video links—that forces the agents into a constructive argument.

Structured Local Intelligence Debate

Your security agent can use `google_places_search` to map out all physical locations for a brand. A performance agent then challenges this data, asking if there are better alternatives using the `google_scholar_search` tool to find academic critiques of those areas. The agents negotiate which sources (local maps vs. academia) carry more weight when making a final recommendation.

Setup guide

Set up ValueSERP 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 ValueSERP 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="ValueSERP_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

The MCP Server provides structured, real-time evidence (like product prices or local addresses) that the agents must debate. They can't make a final call without consulting specific tool outputs.

Yes. One agent can gather general articles via `google_news_search`, while another uses `google_image_search` to provide visual context. The conversation then converges on how these two data streams relate.

The server supplies everything from organic text snippets and academic abstracts (`google_scholar_search`) to direct product links and price points, giving the agents a rich dataset to argue over.

Agents access search queries, image URLs, news articles, place names and coordinates, scholarly abstracts, shopping product details, and video links.

The server only touches public search results (query strings) and location parameters when you invoke the tools. It doesn't require or access any private user account credentials.

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