How to Use the Snov.io MCP in AutoGen

Agents negotiate complex campaigns using AutoGen.

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Connect Snov.io MCP to AutoGen

Create your Vinkius account to connect Snov.io to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Snov.io with AutoGen

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Consensus-Driven Lead Qualification

Set up multiple agents—say, a 'Risk Agent' and a 'Data Agent.' The Data Agent uses `tech_checker` to assess technical feasibility. Meanwhile, the Risk Agent critiques the domain found using `domain_search`, flagging potential issues before any action is taken. They debate until they converge on a qualified target.

Automated Campaign Review Cycle

The MCP Server enables agents to run full verification cycles in dialogue. One agent finds an email with `email_finder`, while another immediately uses `email_verifier` to stress-test it. This collaborative structure ensures that only verified, high-quality data makes it into the final list via `add_prospect`.

Multi-Agent Credit and Prospect Management

Agents can divide labor: Agent A tracks usage by calling `get_balance`, while Agent B manages inputs using `list_prospect_lists`. This negotiation ensures that the system never overspends credits or targets empty/outdated lists.

Setup guide

Set up Snov.io 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 Snov.io 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="Snov.io_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

The MCP Server exposes its tools to multiple agents simultaneously, allowing them to debate the best sequence of actions (e.g., should we `email_finder` or check the tech stack first?).

Yes. You can deploy a debate between two agents: one finds leads using `domain_search`, and the other uses `email_verifier` to argue whether those leads are high enough quality to proceed.

The server handles various structured inputs: domain records, email addresses, technological profiles, and numerical metrics like credit balances. This breadth supports complex deliberation.

Because multiple agents are checking the work—one for speed, one for risk—the final output is more rigorously vetted than a single linear script would be. It's consensus-driven quality control.

The agent setup allows you to define roles that monitor usage, so one agent can flag potential rate limit hits after running multiple `get_prospect_by_email` calls.

Use it with your favorite AI tools

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