How to Use the ZoomInfo MCP in AutoGen

Get consensus on data strategy with AutoGen. Debate ZoomInfo findings across multiple agents.

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Connect ZoomInfo MCP to AutoGen

Create your Vinkius account to connect ZoomInfo 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 ZoomInfo with AutoGen

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Coordinated Market Research

You can deploy a team of specialized agents that debate the best approach for market research. One agent might run `search_companies` while another simultaneously pulls related news using `get_company_news`. The system doesn't just execute tools; it makes multiple roles argue over which data points are most important, converging on a single, reasoned conclusion.

Role-Based Data Validation

Set up an Agent team where one agent is the 'Data Validator.' This agent's sole job is to check if the findings from `enrich_contact` are consistent with other known facts. It forces a second layer of review on sensitive data. This consensus-driven approach prevents single points of failure and ensures that critical information gets scrutinized by multiple perspectives.

Strategic Intent Planning

Use AutoGen to simulate strategic planning. One agent uses `list_intent_topics` to map out potential market areas, and a second agent immediately runs `search_intent`. They negotiate the optimal topics to investigate further. The system guides you through complex decision-making processes where the answer requires deliberation between competing data sets.

Setup guide

Set up ZoomInfo 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 ZoomInfo 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="ZoomInfo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

AutoGen's strength is debate. You assign roles (e.g., 'Researcher,' 'Analyst'). The agents challenge each other, debating which tools—like `search_companies` and `enrich_company`—must be called to achieve consensus on the final data set.

Yes. You define a group of available tools, including all 8 functions from `zoominfo-mcp`. The agents autonomously decide which combination and order of these tools best solves the prompt you give them.

It does. If a simple API call isn't enough, AutoGen’s framework forces deliberation. Agents negotiate between using `search_contacts` versus running a full enrichment cycle, leading to a more robust final output.

It handles structured records (companies/contacts), unstructured news articles (`get_company_news`), and signals derived from `search_intent`—all within the same conversational flow.

The MCP Server provides access to company records, contact identifiers, and market intent signals. The system ensures that multiple agents validate the necessity of accessing these core business facts.

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