How to Use the CUFinder MCP in AutoGen

Q: How do I register CUFinder MCP tools with an AutoGen agent?

You use mcpservertools with StreamableHttpServerParams to fetch the tool definitions. Pass this list to your AssistantAgent constructor, and the adapter handles the schema conversion.

Q: How does the AutoGen adapter handle CUFinder schema types?

The McpToolAdapter automatically translates the MCP tool schemas into AutoGen's native format. This ensures your agents understand the input parameters for tools like bulk enrichment without manual mapping.

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Setup CUFinder with AutoGen

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Resolve Target Accounts inside AutoGen Conversations

The `find_domain` and `find_employees` tools feed raw company profiles directly into your AutoGen multi-agent system. A research agent uses these tools to gather a target company's web address and staff roster before passing the list to a sales agent. Because AutoGen agents discuss and verify information, a coordinator agent can review the retrieved employee list to flag matches that fit your ideal customer profile. This collaborative process ensures you only target high-value leads.

Validate Prospect Data Using an MCP Server Agent

The `verify_email` tool lets a specialized QA agent validate prospect addresses inside your multi-agent conversation. When the lead-generation agent proposes an email, the QA agent runs the verification tool and rejects bad addresses. This consensus-driven approach prevents your outbound agents from drafting emails to dead mailboxes. Your system reaches a decision to send only after both agents agree the address is valid.

Research Tech Stacks via Multi-Agent Debate

The `get_company_tech` and `get_company_revenue` tools provide the raw intelligence your AutoGen agents need to debate account fit. One agent analyzes the prospect's software stack while another evaluates their financial capacity to buy. They negotiate inside the chat thread to determine if the account meets your sales threshold. The conversation concludes with a structured recommendation, completely grounded in the live metrics pulled by the MCP tools.

Setup guide

Set up CUFinder 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 CUFinder 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="CUFinder_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You use `mcp_server_tools` with `StreamableHttpServerParams` to fetch the tool definitions. Pass this list to your `AssistantAgent` constructor, and the adapter handles the schema conversion.

Yes, any agent configured with the tool list can invoke actions like `enrich_linkedin` or `find_phone`. This allows your research and outbound agents to run parallel lookups during their debate.

The `McpToolAdapter` automatically translates the MCP tool schemas into AutoGen's native format. This ensures your agents understand the input parameters for tools like bulk enrichment without manual mapping.

The server supports both stdio and Streamable HTTP transports. For cloud-hosted AutoGen setups, the HTTP transport provides a reliable connection to the Vinkius endpoint.

No, all B2B data like verified emails and phone numbers are processed in isolated V8 sandboxes. Vinkius secures the data transmission end-to-end, meaning your target account lists are never stored or exposed.

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