How to Use the Lusha MCP in AutoGen

Q: Can AutoGen agents use Lusha to search for contacts?

Yes, your agents can call searchcontacts as part of their conversation. They can debate the search results until they reach a consensus.

Q: Will my AutoGen agents automatically check Lusha credits?

You can instruct a specific agent to run getcredits periodically. It acts as a gatekeeper for your API usage.

Coordinate multi-agent sales teams in AutoGen using the Lusha MCP server to debate and verify prospect data.

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Connect Lusha MCP to AutoGen

Create your Vinkius account to connect Lusha 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 Lusha with AutoGen

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Claude

Perplexity

Consensus-Driven Enrichment

Let your agents debate the validity of a lead. By exposing `bulk_enrich` to multiple AutoGen agents, one can request data while another flags it for quality. This ensures that only high-intent leads move forward. Your agents negotiate the enrichment process to balance speed and accuracy.

Deliberate Prospect Lookup

Use `find_person` within a specialized researcher agent. This agent can challenge others if the data retrieved is insufficient for a sales push. It creates a feedback loop where agents refine their search queries. You get better data quality through this internal debate.

Credit-Aware Agent Teams

Assign a budget-monitoring agent to track your Lusha spend. It calls `get_credits` and warns other agents if they approach your API limit. This prevents your team from running out of credits mid-campaign. It keeps your multi-agent system operational without manual intervention.

Setup guide

Set up Lusha 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 Lusha 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="Lusha_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You provide the tool list to the AssistantAgent constructor. The McpToolAdapter handles the conversion so all agents can access the server.

Yes, your agents can call `search_contacts` as part of their conversation. They can debate the search results until they reach a consensus.

The server manages incoming requests via HTTP. It handles multiple agents querying the API simultaneously without bottlenecking your workflow.

You can instruct a specific agent to run `get_credits` periodically. It acts as a gatekeeper for your API usage.

Data is only accessible to the agents involved in the conversation. The MCP server ensures that no unauthorized processes can access your Lusha account.

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